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\n \n 2019\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n How gender and emotions bias the credit decision-making in banking firms.\n \n \n \n \n\n\n \n Bacha, S.; and Azouzi, M., A.\n\n\n \n\n\n\n Journal of Behavioral and Experimental Finance, 22: 183-191. 6 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {How gender and emotions bias the credit decision-making in banking firms},\n type = {article},\n year = {2019},\n pages = {183-191},\n volume = {22},\n websites = {https://www.sciencedirect.com/science/article/pii/S2214635018302739#!},\n month = {6},\n publisher = {Elsevier},\n day = {1},\n id = {04a85d23-9076-31eb-8b8d-1ab015333467},\n created = {2019-03-26T13:11:32.294Z},\n accessed = {2019-03-26},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2019-03-26T13:11:32.294Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This study sheds the light on the effect of the emotional bias and the gender on the credit risk management of Tunisian banks. We may expect that male and female CEO react differently to emotions and that gender-based behavior differences will affect the organizational design of the credit decision making. We opt for a Bayesian Net Work method which uses the variables to express the events or objects and analyze their behaviors to model such causal relationships. Results show that emotional bias can explain the cross-sectional heterogeneity in risk-taking behavior among banks and that managers’ gender types influences the propensity to delegate the credit decision making. Overconfident and optimist female banks’ manager are more conservative than males and they tend to centralize the credit decision-making process. Findings show also that financial literacy significatively affect the credit decision making, whereas bank size have no effect.},\n bibtype = {article},\n author = {Bacha, Sami and Azouzi, Mohamed Ali},\n doi = {10.1016/J.JBEF.2019.03.004},\n journal = {Journal of Behavioral and Experimental Finance}\n}

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\n \n\n \n \n \n \n \n A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach.\n \n \n \n\n\n \n Hosseini, S.; and Ivanov, D.\n\n\n \n\n\n\n Annals of Operations Research. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{\n title = {A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach},\n type = {article},\n year = {2019},\n publisher = {Springer US},\n id = {84d98d53-ce82-3894-bd35-8f3e054d6e1e},\n created = {2019-09-30T14:09:25.937Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2019-09-30T14:09:25.937Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case- study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.},\n bibtype = {article},\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry},\n doi = {10.1007/s10479-019-03350-8},\n journal = {Annals of Operations Research}\n}

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\n \n\n \n \n \n \n \n \n Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach.\n \n \n \n \n\n\n \n Hosseini, S.; Ivanov, D.; and Dolgui, A.\n\n\n \n\n\n\n International Journal of Production Research, 0(0): 1-19. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Ripple$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach},\n type = {article},\n year = {2019},\n keywords = {Bayesian methods,Markov modelling,ripple effect,supply chain dynamics,supply chain resilience,supply chain risk management},\n pages = {1-19},\n volume = {0},\n websites = {https://doi.org/00207543.2019.1661538},\n publisher = {Taylor & Francis},\n id = {d3fb7dcb-d50c-3ed3-8917-3165bdad2223},\n created = {2019-09-30T14:09:26.092Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2019-09-30T14:09:26.092Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Hosseini, Seyedmohsen and Ivanov, Dmitry and Dolgui, Alexandre},\n doi = {10.1080/00207543.2019.1661538},\n journal = {International Journal of Production Research},\n number = {0}\n}

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\n \n 2018\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution.\n \n \n \n \n\n\n \n Li, S.; Wu, Y.; Xu, Y.; Hu, J.; and Hu, J.\n\n\n \n\n\n\n Applied Sciences, 8(4): 493. 3 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution},\n type = {article},\n year = {2018},\n keywords = {Bayesian network,adaptability design,data analysis,product function evolution},\n pages = {493},\n volume = {8},\n websites = {http://www.mdpi.com/2076-3417/8/4/493},\n month = {3},\n publisher = {Multidisciplinary Digital Publishing Institute},\n day = {26},\n id = {af597384-33db-3cf3-b66f-d25167b2f227},\n created = {2018-03-31T22:52:46.347Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2018-03-31T22:52:46.347Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process.},\n bibtype = {article},\n author = {Li, Shaobo and Wu, Yongming and Xu, Yanxia and Hu, Jie and Hu, Jianjun},\n doi = {10.3390/app8040493},\n journal = {Applied Sciences},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects.\n \n \n \n \n\n\n \n Khakzad, N.; and Reniers, G.\n\n\n \n\n\n\n Reliability Engineering & System Safety. 3 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Low-capacity$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Low-capacity utilization of process plants: A cost-robust approach to tackle man-made domino effects},\n type = {article},\n year = {2018},\n websites = {http://linkinghub.elsevier.com/retrieve/pii/S0951832017309158},\n month = {3},\n id = {ab11c41b-ba65-3d9c-a913-f4a5d1b4cbd3},\n created = {2018-03-31T23:32:40.352Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2018-03-31T23:32:40.352Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.},\n bibtype = {article},\n author = {Khakzad, Nima and Reniers, Genserik},\n doi = {10.1016/j.ress.2018.03.030},\n journal = {Reliability Engineering & System Safety}\n}

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\n \n\n \n \n \n \n \n \n Systemic methodology for risks evaluation and management in the energy and mining sectors (SYSMEREM-EMS) using bayesian networks.\n \n \n \n \n\n\n \n Rodriguez-Ulloa, R.\n\n\n \n\n\n\n Journal of Decision Systems, 27(sup1): 191-200. 5 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Systemic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Systemic methodology for risks evaluation and management in the energy and mining sectors (SYSMEREM-EMS) using bayesian networks},\n type = {article},\n year = {2018},\n keywords = {Bayesian Networks (BN),Peru,Risks evaluation and management,Soft Systems Methodology (SSM),System Dynamics’ (SD) causal diagrams,energy sector,expert systems,mining sector},\n pages = {191-200},\n volume = {27},\n websites = {https://www.tandfonline.com/doi/full/10.1080/12460125.2018.1468157},\n month = {5},\n publisher = {Taylor & Francis},\n day = {15},\n id = {52ab395f-afd2-3fd6-a1ed-69577cd24047},\n created = {2018-12-07T00:38:50.990Z},\n accessed = {2018-11-23},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2018-12-07T00:38:50.990Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {AbstractThe paper’s purpose was to show a systemic methodology for risks evaluation and management in the energy and mining sectors (SYSMEREM-EM) and its application in the Peruvian context. For this purpose, this paper shows the combination of systemic approaches and artificial intelligence technology. Thus, for the processes’ modelling of the value chains in both sectors, Soft Systems Methodology (SSM) was used; for the elucidation of the risks causalities embedded in dangerous events existing in the processes, System Dynamics’ causal diagrams were used; and for risks’ evaluation and management, the application of Bayesian networks’ expert systems for decision-making was the approach used. These three techniques were used, in this sequence, within an overall framework. The paper ends with some comments about lessons learned and recommendations for further research.},\n bibtype = {article},\n author = {Rodriguez-Ulloa, Ricardo},\n doi = {10.1080/12460125.2018.1468157},\n journal = {Journal of Decision Systems},\n number = {sup1}\n}

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\n \n 2016\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Does Managerial Emotional Biases Affect Debt Maturity Preference? Bayesian Network Method: Evidence from Tunisia.\n \n \n \n \n\n\n \n Ali, A., M.; and Anis, J.\n\n\n \n\n\n\n Financial Risk and Management Reviews, 2(1): 1-25. 6 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Does$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Does Managerial Emotional Biases Affect Debt Maturity Preference? Bayesian Network Method: Evidence from Tunisia},\n type = {article},\n year = {2016},\n pages = {1-25},\n volume = {2},\n websites = {http://www.pakinsight.com/archive/89/06-2016/1},\n month = {6},\n day = {1},\n id = {aa99f3db-d934-3dab-9d2a-21354b812ac8},\n created = {2016-09-03T19:40:19.000Z},\n accessed = {2016-08-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This study documents that managerial characteristics’ play an important role in determining\r\ncorporate debt maturity. Specifically, we focus on the relationship between the managerial biases\r\nand firm debt maturity preference. Empirical analysis of the relationship between emotional bias\r\nand debt maturity using Bayesian Network Method. We distributed a questionnaire among 100\r\nTunisian managers to measure their emotional biases. Our results have revealed that the\r\nbehavioral analysis of debt maturity preference implies leader affected by behavioral biases\r\n(optimism, loss aversion, and overconfidence) presence prefer long term debt maturity allowing\r\nthis protect against the takeover operation Russianness. },\n bibtype = {article},\n author = {Ali, AZOUZI Mohamed and Anis, JARBOUI},\n doi = {10.18488/journal.89/2016.2.1/89.1.1.25},\n journal = {Financial Risk and Management Reviews},\n number = {1}\n}

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\n\n\n\n\n\n

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\n \n 2015\n \n \n (7)\n \n \n

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\n \n\n \n \n \n \n \n \n Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application.\n \n \n \n \n\n\n \n Baraldi, P.; Podofillini, L.; Mkrtchyan, L.; Zio, E.; and Dang, V., N.\n\n\n \n\n\n\n Reliability Engineering & System Safety, 138: 176-193. 6 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application},\n type = {article},\n year = {2015},\n keywords = {Bayesian belief networks,Dependence assessment,Expert judgement,Expert models,Fuzzy logic,Human reliability analysis},\n pages = {176-193},\n volume = {138},\n websites = {http://www.sciencedirect.com/science/article/pii/S0951832015000265},\n month = {6},\n id = {bc0909f0-b42d-35f3-993a-3a9a1a54a376},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, human reliability analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty.},\n bibtype = {article},\n author = {Baraldi, Piero and Podofillini, Luca and Mkrtchyan, Lusine and Zio, Enrico and Dang, Vinh N.},\n doi = {10.1016/j.ress.2015.01.016},\n journal = {Reliability Engineering & System Safety}\n}

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\n \n\n \n \n \n \n \n \n A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map.\n \n \n \n \n\n\n \n Wee, Y., Y.; Cheah, W., P.; Tan, S., C.; and Wee, K.\n\n\n \n\n\n\n Expert Systems with Applications, 42(1): 468-487. 1 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map},\n type = {article},\n year = {2015},\n keywords = {Bayesian belief network,Causal knowledge,Causal reasoning,Fuzzy cognitive map,Root cause analysis,Soft computing},\n pages = {468-487},\n volume = {42},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417414003789},\n month = {1},\n id = {ea9e9eba-d958-3b49-b0c0-22c5d70603dc},\n created = {2015-04-11T19:07:36.000Z},\n accessed = {2014-12-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {People often want to know the root cause of things and events in certain application domains such as intrusion detection, medical diagnosis, and fault diagnosis. In many of these domains, a large amount of data is available. The problem is how to perform root cause analysis by leveraging the data asset at hand. Root cause analysis consists of two main functions, diagnosis of the root cause and prognosis of the effect. In this paper, a method for root cause analysis is proposed. In the first phase, a causal knowledge model is constructed by learning a Bayesian belief network (BBN) from data. BBN’s backward and forward inference mechanisms are used for the diagnosis and prognosis of the root cause. Despite its powerful reasoning capability, the representation of causal strength in BBN as a set of probability values in a conditional probability table (CPT) is not intuitive at all. It is at its worst when the number of probability values needed grows exponentially with the number of variables involved. Conversely, a fuzzy cognitive map (FCM) can provide an intuitive interface as the causal strength is simply represented by a single numerical value. Hence, in the second phase of the method, an intuitive interface using FCM is generated from the BBN-based causal knowledge model, applying the migration framework proposed and formulated in this paper.},\n bibtype = {article},\n author = {Wee, Yit Yin and Cheah, Wooi Ping and Tan, Shing Chiang and Wee, KuokKwee},\n doi = {10.1016/j.eswa.2014.06.037},\n journal = {Expert Systems with Applications},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n A framework for model integration and holistic modelling of socio-technical systems.\n \n \n \n \n\n\n \n Wu, P., P.; Fookes, C.; Pitchforth, J.; and Mengersen, K.\n\n\n \n\n\n\n Decision Support Systems, 71: 14-27. 3 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A framework for model integration and holistic modelling of socio-technical systems},\n type = {article},\n year = {2015},\n keywords = {Agent Based Model,Bayesian Network,Business Process Modelling Notation,Modelling,Socio-technical systems (STS)},\n pages = {14-27},\n volume = {71},\n websites = {http://www.sciencedirect.com/science/article/pii/S016792361500007X},\n month = {3},\n id = {42e86599-d7eb-307c-8fda-e0ab1092ab2e},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-02-04},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a layered framework for the purposes of integrating different socio-technical systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another are obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.},\n bibtype = {article},\n author = {Wu, Paul Pao-Yen and Fookes, Clinton and Pitchforth, Jegar and Mengersen, Kerrie},\n doi = {10.1016/j.dss.2015.01.006},\n journal = {Decision Support Systems}\n}

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\n \n\n \n \n \n \n \n \n Application of a Bayesian Network complex system model to a successful community electricity demand reduction program.\n \n \n \n \n\n\n \n Morris, P.; Vine, D.; and Buys, L.\n\n\n \n\n\n\n Energy. 3 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Application$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Application of a Bayesian Network complex system model to a successful community electricity demand reduction program},\n type = {article},\n year = {2015},\n keywords = {Bayesian network,Complex systems model,Multi-disciplinary,Peak demand,Residential electricity use},\n websites = {http://www.sciencedirect.com/science/article/pii/S0360544215001711},\n month = {3},\n id = {cc1022d0-2380-3837-899f-e0b541a77184},\n created = {2015-04-11T20:33:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.},\n bibtype = {article},\n author = {Morris, Peter and Vine, Desley and Buys, Laurie},\n doi = {10.1016/j.energy.2015.02.019},\n journal = {Energy}\n}

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\n \n\n \n \n \n \n \n \n Development of recursive decision making model in bilateral construction procurement negotiation.\n \n \n \n \n\n\n \n Leu, S.; Pham, V., H., S.; and Pham, T., H., N.\n\n\n \n\n\n\n Automation in Construction, 53: 131-140. 5 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Development of recursive decision making model in bilateral construction procurement negotiation},\n type = {article},\n year = {2015},\n keywords = {Bayesian network,Bilateral negotiation,Construction procurement,Decision support system,Game theory},\n pages = {131-140},\n volume = {53},\n websites = {http://www.sciencedirect.com/science/article/pii/S0926580515000503},\n month = {5},\n id = {ec0cbaf0-2af7-3cd5-8b7e-513426f4230d},\n created = {2015-04-11T20:33:13.000Z},\n accessed = {2015-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Price negotiation in construction procurement is a form of decision making where contractor and supplier jointly search for a mutually agreed solution. In price negotiation, with information available about the agent's preferences, a negotiation may result in a mutually beneficial agreement. However, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a beneficial agreement. In order to overcome this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent's preference in bilateral negotiation. The proposed approach employs Bayesian theory to analyze the opponent's historical offers and to approximately predict the opponent's preference over negotiation issue. A Nash equilibrium algorithm is also integrated into the prediction approach to help agents on how to propose beneficial offers based on the prediction results. Validation results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency.},\n bibtype = {article},\n author = {Leu, Sou-Sen and Pham, Vu Hong Son and Pham, Thi Hong Nhung},\n doi = {10.1016/j.autcon.2015.03.016},\n journal = {Automation in Construction}\n}

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\n \n\n \n \n \n \n \n \n A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data.\n \n \n \n \n\n\n \n Sun, J.; and Sun, J.\n\n\n \n\n\n\n Transportation Research Part C: Emerging Technologies, 54: 176-186. 5 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data},\n type = {article},\n year = {2015},\n keywords = {Dynamic Bayesian network,Real-time crash prediction,Speed conditions data,Traffic states,Urban expressway},\n pages = {176-186},\n volume = {54},\n websites = {http://www.sciencedirect.com/science/article/pii/S0968090X15000856},\n month = {5},\n id = {51e4db95-f94f-3a1d-a97d-d5d16ebb30cc},\n created = {2015-04-11T20:33:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.},\n bibtype = {article},\n author = {Sun, Jie and Sun, Jian},\n doi = {10.1016/j.trc.2015.03.006},\n journal = {Transportation Research Part C: Emerging Technologies}\n}

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\n \n\n \n \n \n \n \n \n An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans.\n \n \n \n \n\n\n \n Lee, C.; Song, B.; and Park, Y.\n\n\n \n\n\n\n Technological Forecasting and Social Change, 90: 285-301. 1 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans},\n type = {article},\n year = {2015},\n keywords = {Bayesian network,Future changes,Ripple impacts,Scenario-based technology roadmapping,Sensitivity analysis,Uncertainty},\n pages = {285-301},\n volume = {90},\n websites = {http://www.sciencedirect.com/science/article/pii/S0040162513003272},\n month = {1},\n id = {b90bf56c-565c-3b34-8d82-c8a1ac0956e3},\n created = {2015-04-11T22:23:04.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Scenario-based technology roadmapping offers a strong capability for strategic planning to respond to increasingly volatile environments. However, previous studies cannot guide organisations towards making robust decisions against complex future conditions since they remain conceptual and rely solely on graphical mapping tools. To counter this, we propose a systematic approach to making scenario-based technology roadmapping more robust by adding the ability to assess the impacts of future changes on organisational plans. At the heart of the suggested approach is a Bayesian network that can examine uncertainty inherent in future changes and ripple impacts resulting from interdependence among activities. The proposed approach is designed to be executed in three discrete steps: defining a roadmap topology and causal relationships via qualitative and quantitative modelling; assessing the impacts of future changes on organisational plans via current state analysis and sensitivity analysis; and finally managing plans and activities via development of plan assessment map and activity assessment map. A case study of photovoltaic cell technology is presented to show the feasibility of our method. We believe the systematic process and quantitative outcomes the suggested approach offers can facilitate responsive technology planning in the face of future uncertainties.},\n bibtype = {article},\n author = {Lee, Changyong and Song, Bomi and Park, Yongtae},\n doi = {10.1016/j.techfore.2013.12.020},\n journal = {Technological Forecasting and Social Change}\n}

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\n \n 2014\n \n \n (11)\n \n \n

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\n \n\n \n \n \n \n \n \n Extracting Activity-travel Diaries from GPS Data: Towards Integrated Semi-automatic Imputation.\n \n \n \n \n\n\n \n Feng, T.; and Timmermans, H., J.\n\n\n \n\n\n\n Procedia Environmental Sciences, 22: 178-185. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Extracting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Extracting Activity-travel Diaries from GPS Data: Towards Integrated Semi-automatic Imputation},\n type = {article},\n year = {2014},\n keywords = {Activity-travel diary,Bayesian Belief Network,imputation},\n pages = {178-185},\n volume = {22},\n websites = {http://www.sciencedirect.com/science/article/pii/S1878029614001650},\n id = {5174fb21-b68f-3423-8f0b-c31d51db9459},\n created = {2015-04-11T18:12:59.000Z},\n accessed = {2015-03-20},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents an integrated approach to extracting activity-travel diaries from GPS data. The imputation involves a semi-automatic procedure of transportation mode and activity type recognition, and applies full and partial consistency principles to different trip episodes of a tour. Complementing earlier work on the evaluation of this approach at the epoch level, this paper investigates the performance of the integrated imputation at the episode level. The originally imputed data were used as reference to compare the superimposed data against validated data. Results indicate that the distribution of transportation modes and activity types are similar for these data sets. The new algorithm imputes diary data that are closer to the validated data that the results of the original algorithm.},\n bibtype = {article},\n author = {Feng, Tao and Timmermans, Harry J.P.},\n doi = {10.1016/j.proenv.2014.11.018},\n journal = {Procedia Environmental Sciences}\n}

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\n \n\n \n \n \n \n \n \n A framework for risk assessment for maritime transportation systems—A case study for open sea collisions involving RoPax vessels.\n \n \n \n \n\n\n \n Montewka, J.; Ehlers, S.; Goerlandt, F.; Hinz, T.; Tabri, K.; and Kujala, P.\n\n\n \n\n\n\n Reliability Engineering & System Safety, 124: 142-157. 4 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A framework for risk assessment for maritime transportation systems—A case study for open sea collisions involving RoPax vessels},\n type = {article},\n year = {2014},\n keywords = {Bayesian Belief Networks,F–N diagram,Maritime transportation,Risk analysis,RoPax safety,Ship–ship collision},\n pages = {142-157},\n volume = {124},\n websites = {http://www.sciencedirect.com/science/article/pii/S0951832013003116},\n month = {4},\n id = {1b1baff3-0e98-3495-a288-9d63304e6080},\n created = {2015-04-11T18:46:34.000Z},\n accessed = {2014-12-20},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Maritime accidents involving ships carrying passengers may pose a high risk with respect to human casualties. For effective risk mitigation, an insight into the process of risk escalation is needed. This requires a proactive approach when it comes to risk modelling for maritime transportation systems. Most of the existing models are based on historical data on maritime accidents, and thus they can be considered reactive instead of proactive. This paper introduces a systematic, transferable and proactive framework estimating the risk for maritime transportation systems, meeting the requirements stemming from the adopted formal definition of risk. The framework focuses on ship–ship collisions in the open sea, with a RoRo/Passenger ship (RoPax) being considered as the struck ship. First, it covers an identification of the events that follow a collision between two ships in the open sea, and, second, it evaluates the probabilities of these events, concluding by determining the severity of a collision. The risk framework is developed with the use of Bayesian Belief Networks and utilizes a set of analytical methods for the estimation of the risk model parameters. Finally, a case study is presented, in which the risk framework developed here is applied to a maritime transportation system operating in the Gulf of Finland (GoF). The results obtained are compared to the historical data and available models, in which a RoPax was involved in a collision, and good agreement with the available records is found.},\n bibtype = {article},\n author = {Montewka, Jakub and Ehlers, Sören and Goerlandt, Floris and Hinz, Tomasz and Tabri, Kristjan and Kujala, Pentti},\n doi = {10.1016/j.ress.2013.11.014},\n journal = {Reliability Engineering & System Safety}\n}

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\n \n\n \n \n \n \n \n \n An extended systematic literature review on provision of evidence for safety certification.\n \n \n \n \n\n\n \n Nair, S.; de la Vara, J., L.; Sabetzadeh, M.; and Briand, L.\n\n\n \n\n\n\n Information and Software Technology, 56(7): 689-717. 7 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An extended systematic literature review on provision of evidence for safety certification},\n type = {article},\n year = {2014},\n keywords = {AADL,ACRuDA,ASA,ASCAD,Adelard Safety Case Development,Architecture Analysis & Design Language,Assessment and Certification Rules for Digital Arc,Automated and Structured Analysis,BBN,Bayesian Belief Networks,CAE,CCS,CDL,CENELEC,CMA,COTS,CSP,Calculus of Communicating Systems,Claims, Arguments and Evidence,Comité Européen de Normalisation Electrotechnique,Commercial Off-The-Shelf,Common Mode Analysis,Communicating Sequential Processes,Configuration Deviation List,DECOS,DOVE,Dependable Embedded COmponents and Systems,Design Oriented Verification and Evaluation,ECHA,EMFI,ETA,EVA,Electromagnetic Fault Injection,Environmental Condition Hazard Assessment,Event Tree Analysis,Evidence Volume Approach,FFA,FFPA,FHA,FMEA,FMECA,FMEDA,FMES,FPGA,FPTC,FPTN,FSM,FTA,Failure Mode and Effect Summary,Failure Mode, Effects Analysis,Failure Mode, Effects and Criticality Analysis,Failure Modes, Effects and Diagnostic Coverage Ana,Failure Propagation and Transformation Notation,Fault Propagation and Transformation Calculus,Fault Tree Analysis,Field-programmable gate array,Functional Failure Analysis,Functional Failure Patch Analysis,Functional Hazard Analysis,Functional Safety Management,GQM,GSN,Goal Question Metric,Goal Structuring Notation,HAZID,HAZOP,HAZard and Operability,HEP,HHA,HOL,HRA,Hazard Identification Study,Higher Order Logic,Human Error Prediction,Human Hazard Analysis,Human Reliability Analysis,IEC,IET,IHA,ISO,Institution of Engineering and Technology,International Electro-technical Commission,International Organization for Standardization,Intrinsic Hazard Analysis,MC/DC,MDE,MMEL,MTBF,MTTF,Master Minimum Equipment List,Mean Time Between Failures,Mean Time To Failure,Model-Driven Engineering,Modified Condition/Decision Coverage,OCL,OS,Object Constraint Language,Operating System,PHA,PRA,PS,PSAC,Particular Risk Analysis,Plan for Software Aspects of Certification,Preliminary Hazard Analysis,Primary Study,QA,Quality Assurance,RASP,RTCA,RTOS,Radio Technical Commission for Aeronautics,Real-Time OS,Risk Assessment of Structural Part,SACM,SAL,SAS,SCMP,SDP,SEAL,SHARD,SIL,SLR,SQA,SRS,SSG,SVP,SWIFI,Safety Assurance Level,Safety Evidence Assurance Level,Safety Integrity Level,Safety Specification Graph,Safety certification,Safety compliance,Safety evidence,Safety standards,Safety–critical systems,Software Accomplishment Summary,Software Configuration Management Plan,Software Development Plan,Software Hazard Analysis and Resolution in Design,Software Implemented Fault Injection,Software QA,Software Requirements Specification,Software Verification Plan,Structured Assurance Case Metamodel,Systematic Literature Review,Systematic literature review,TPTP,Thousands of Problems for Theorem Provers,UAS,Unmanned Autonomous Systems,V&V,Verification and Validation},\n pages = {689-717},\n volume = {56},\n websites = {http://www.sciencedirect.com/science/article/pii/S0950584914000603},\n month = {7},\n id = {2233a01e-ae9d-3cb1-b204-3a5cac1c919b},\n created = {2015-04-11T18:56:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {CONTEXT\nCritical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing undue risks to the user, the public, or the environment. Safety is typically ensured via complying with safety standards. Demonstrating compliance to these standards involves providing evidence to show that the safety criteria of the standards are met. \n\nOBJECTIVE\nIn order to cope with the complexity of large critical systems and subsequently the plethora of evidence information required for achieving compliance, safety professionals need in-depth knowledge to assist them in classifying different types of evidence, and in structuring and assessing the evidence. This paper is a step towards developing such a body of knowledge that is derived from a large-scale empirically rigorous literature review. \n\nMETHOD\nWe use a Systematic Literature Review (SLR) as the basis for our work. The SLR builds on 218 peer-reviewed studies, selected through a multi-stage process, from 4963 studies published between 1990 and 2012. \n\nRESULTS\nWe develop a taxonomy that classifies the information and artefacts considered as evidence for safety. We review the existing techniques for safety evidence structuring and assessment, and further study the relevant challenges that have been the target of investigation in the academic literature. We analyse commonalities in the results among different application domains and discuss implications of the results for both research and practice. \n\nCONCLUSION\nThe paper is, to our knowledge, the largest existing study on the topic of safety evidence. The results are particularly relevant to practitioners seeking a better grasp on evidence requirements as well as to researchers in the area of system safety. As a major finding of the review, the results strongly suggest the need for more practitioner-oriented and industry-driven empirical studies in the area of safety certification.},\n bibtype = {article},\n author = {Nair, Sunil and de la Vara, Jose Luis and Sabetzadeh, Mehrdad and Briand, Lionel},\n doi = {10.1016/j.infsof.2014.03.001},\n journal = {Information and Software Technology},\n number = {7}\n}

\n

\n\n\n

\n CONTEXT\nCritical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing undue risks to the user, the public, or the environment. Safety is typically ensured via complying with safety standards. Demonstrating compliance to these standards involves providing evidence to show that the safety criteria of the standards are met. \n\nOBJECTIVE\nIn order to cope with the complexity of large critical systems and subsequently the plethora of evidence information required for achieving compliance, safety professionals need in-depth knowledge to assist them in classifying different types of evidence, and in structuring and assessing the evidence. This paper is a step towards developing such a body of knowledge that is derived from a large-scale empirically rigorous literature review. \n\nMETHOD\nWe use a Systematic Literature Review (SLR) as the basis for our work. The SLR builds on 218 peer-reviewed studies, selected through a multi-stage process, from 4963 studies published between 1990 and 2012. \n\nRESULTS\nWe develop a taxonomy that classifies the information and artefacts considered as evidence for safety. We review the existing techniques for safety evidence structuring and assessment, and further study the relevant challenges that have been the target of investigation in the academic literature. We analyse commonalities in the results among different application domains and discuss implications of the results for both research and practice. \n\nCONCLUSION\nThe paper is, to our knowledge, the largest existing study on the topic of safety evidence. The results are particularly relevant to practitioners seeking a better grasp on evidence requirements as well as to researchers in the area of system safety. As a major finding of the review, the results strongly suggest the need for more practitioner-oriented and industry-driven empirical studies in the area of safety certification.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Applying a validation framework to a working airport terminal model.\n \n \n \n \n\n\n \n Pitchforth, J.; Wu, P.; and Mengersen, K.\n\n\n \n\n\n\n Expert Systems with Applications, 41(9): 4388-4400. 7 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Applying$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Applying a validation framework to a working airport terminal model},\n type = {article},\n year = {2014},\n keywords = {Airport,Application,Bayesian Belief Network,Systems model,Validation},\n pages = {4388-4400},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417414000256},\n month = {7},\n id = {5573a08c-4587-34f2-9744-dd8689174708},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Validation is an important issue in the development and application of Bayesian Belief Network (BBN) models, especially when the outcome of the model cannot be directly observed. Despite this, few frameworks for validating BBNs have been proposed and fewer have been applied to substantive real-world problems. In this paper we adopt the approach by Pitchforth and Mengersen (2013), which includes nine validation tests that each focus on the structure, discretisation, parameterisation and behaviour of the BBNs included in the case study. We describe the process and result of implementing a validation framework on a model of a real airport terminal system with particular reference to its effectiveness in producing a valid model that can be used and understood by operational decision makers. In applying the proposed validation framework we demonstrate the overall validity of the Inbound Passenger Facilitation Model as well as the effectiveness of the validity framework itself.},\n bibtype = {article},\n author = {Pitchforth, Jegar and Wu, Paul and Mengersen, Kerrie},\n doi = {10.1016/j.eswa.2014.01.013},\n journal = {Expert Systems with Applications},\n number = {9}\n}

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\n \n\n \n \n \n \n \n \n Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks.\n \n \n \n \n\n\n \n Amundson, J.; Brown, A.; Grabowski, M.; and Badurdeen, F.\n\n\n \n\n\n\n Procedia CIRP, 17: 320-325. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Life-cycle$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks},\n type = {article},\n year = {2014},\n keywords = {Bayesian Belief Network,Life-Cycle,Supply Chain Risk},\n pages = {320-325},\n volume = {17},\n websites = {http://www.sciencedirect.com/science/article/pii/S2212827114002881},\n id = {11efbf7a-9efd-343e-b246-cac40618f006},\n created = {2015-04-11T19:07:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.},\n bibtype = {article},\n author = {Amundson, Joseph and Brown, Adam and Grabowski, Matthias and Badurdeen, Fazleena},\n doi = {10.1016/j.procir.2014.02.029},\n journal = {Procedia CIRP}\n}

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\n \n\n \n \n \n \n \n \n Decision-network polynomials and the sensitivity of decision-support models.\n \n \n \n \n\n\n \n Borgonovo, E.; and Tonoli, F.\n\n\n \n\n\n\n European Journal of Operational Research, 239(2): 490-503. 12 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision-network$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Decision-network polynomials and the sensitivity of decision-support models},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Decision analysis,Decision trees,Influence diagrams,Sensitivity analysis},\n pages = {490-503},\n volume = {239},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221714004226},\n month = {12},\n id = {d8dc1142-0f34-3c11-9b74-78675bf70519},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Decision makers benefit from the utilization of decision-support models in several applications. Obtaining managerial insights is essential to better inform the decision-process. This work offers an in-depth investigation into the structural properties of decision-support models. We show that the input–output mapping in influence diagrams, decision trees and decision networks is piecewise multilinear. The conditions under which sensitivity information cannot be extracted through differentiation are examined in detail. By complementing high-order derivatives with finite change sensitivity indices, we obtain a systematic approach that allows analysts to gain a wide range of managerial insights. A well-known case study in the medical sector illustrates the findings.},\n bibtype = {article},\n author = {Borgonovo, Emanuele and Tonoli, Fabio},\n doi = {10.1016/j.ejor.2014.05.015},\n journal = {European Journal of Operational Research},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Modeling method of cascading crisis events based on merging Bayesian Network.\n \n \n \n \n\n\n \n Qiu, J.; Wang, Z.; Ye, X.; Liu, L.; and Dong, L.\n\n\n \n\n\n\n Decision Support Systems, 62: 94-105. 6 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Modeling method of cascading crisis events based on merging Bayesian Network},\n type = {article},\n year = {2014},\n keywords = {Bayesian network,Cascading crisis events,Crisis management,Modeling method},\n pages = {94-105},\n volume = {62},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923614001043},\n month = {6},\n id = {67631bd4-04c7-31c1-8a50-971e6e180c42},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a Bayesian Network (BN)-based modeling method for cascading crisis events. Crisis events have occurred more frequently in recent years, such as typhoons, rainstorms, and floods, posing a great threat to humans. Addressing these crises requires a more effective method for crisis early-warning and disaster mitigation in crisis management. However, few modeling methods can combine the crisis chain reaction (macro-view) and the elements within the crisis event (micro-view) in a cascading crisis events. Existing classical methods fail to consider the causal relations linking the micro to macro level in crisis events, which affects the forecasting accuracy and effectiveness. Based on systems theory, this paper first abstracts the crisis event as a three-layer structure model consisting of input elements, state elements and output elements from a micro-view. Next, a cascading crisis events Bayesian Network (CCEBN) model is developed by merging the single crisis events Bayesian Networks (SCEBNs). This method efficiently combines the crisis event's micro-view and the macro-view. The proposed BN-based model makes it possible to forecast and analyze the chain reaction path and the potential losses due to a crisis event. Finally, sample application is provided to illustrate the utility of the model. The experimental results indicate that the method can effectively improve the forecasting accuracy.},\n bibtype = {article},\n author = {Qiu, Jiangnan and Wang, Zhiqiang and Ye, Xin and Liu, Lili and Dong, Leilei},\n doi = {10.1016/j.dss.2014.03.007},\n journal = {Decision Support Systems}\n}

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\n \n\n \n \n \n \n \n \n An intelligent situation awareness support system for safety-critical environments.\n \n \n \n \n\n\n \n Naderpour, M.; Lu, J.; and Zhang, G.\n\n\n \n\n\n\n Decision Support Systems, 59: 325-340. 3 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An intelligent situation awareness support system for safety-critical environments},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Cognition-driven decision support,Decision support systems,Risk assessment,Situation assessment,Situation awareness},\n pages = {325-340},\n volume = {59},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923614000050},\n month = {3},\n id = {8735c212-b415-353e-9713-f35e26adb4c9},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-03-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of four major elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level.},\n bibtype = {article},\n author = {Naderpour, Mohsen and Lu, Jie and Zhang, Guangquan},\n doi = {10.1016/j.dss.2014.01.004},\n journal = {Decision Support Systems}\n}

\n

\n\n\n

\n Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of four major elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Cycle-by-cycle intersection queue length distribution estimation using sample travel times.\n \n \n \n \n\n\n \n Hao, P.; Ban, X., (.; Guo, D.; and Ji, Q.\n\n\n \n\n\n\n Transportation Research Part B: Methodological, 68: 185-204. 10 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Cycle-by-cycle$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Cycle-by-cycle intersection queue length distribution estimation using sample travel times},\n type = {article},\n year = {2014},\n keywords = {Bayesian Networks,Cycle-by-cycle queue length distribution,Intersection travel times,Mobile sensors,Signalized intersections},\n pages = {185-204},\n volume = {68},\n websites = {http://www.sciencedirect.com/science/article/pii/S0191261514001118},\n month = {10},\n id = {d73e4c66-c09c-3ca4-8ec3-9a6446fc9c26},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-03-20},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We propose Bayesian Network based methods for estimating the cycle by cycle queue length distribution of a signalized intersection. Queue length here is defined as the number of vehicles in a cycle which have experienced significant delays. The data input to the methods are sample travel times from mobile traffic sensors collected between an upstream location and a downstream location of the intersection. The proposed methods first classify traffic conditions and sample scenarios to seven cases. BN models are then developed for each case. The methods are tested using data from NGSIM, a field experiment, and microscopic traffic simulation. The results are satisfactory compared with two specific queue length estimation methods previously developed in the literature.},\n bibtype = {article},\n author = {Hao, Peng and Ban, Xuegang (Jeff) and Guo, Dong and Ji, Qiang},\n doi = {10.1016/j.trb.2014.06.004},\n journal = {Transportation Research Part B: Methodological}\n}

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\n\n\n

\n \n\n \n \n \n \n \n \n A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling.\n \n \n \n \n\n\n \n Wu, P., P.; Pitchforth, J.; and Mengersen, K.\n\n\n \n\n\n\n Transportation Research Part C: Emerging Technologies, 46: 247-260. 9 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling},\n type = {article},\n year = {2014},\n keywords = {Airport,Bayesian Network,Complex systems,Dynamic system,Modelling,Passenger facilitation},\n pages = {247-260},\n volume = {46},\n websites = {http://www.sciencedirect.com/science/article/pii/S0968090X14001181},\n month = {9},\n id = {8bd2703a-532a-3779-b311-b7ad4a7400f9},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.},\n bibtype = {article},\n author = {Wu, Paul Pao-Yen and Pitchforth, Jegar and Mengersen, Kerrie},\n doi = {10.1016/j.trc.2014.05.005},\n journal = {Transportation Research Part C: Emerging Technologies}\n}

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\n \n\n \n \n \n \n \n \n Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items.\n \n \n \n \n\n\n \n Khodakarami, V.; and Abdi, A.\n\n\n \n\n\n\n International Journal of Project Management, 32(7): 1233-1245. 10 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Project$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Common cause,Dependency,Project cost analysis},\n pages = {1233-1245},\n volume = {32},\n websites = {http://www.sciencedirect.com/science/article/pii/S0263786314000027},\n month = {10},\n id = {52bb9a92-9454-3906-a9ed-0415077e5c5b},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2014-12-28},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Uncertainty of cost items is an important aspect of complex projects. Cost uncertainty analysis aims to help decision makers to understand and model different factors affecting funding exposure and ultimately estimate the cost of project. The common practice in cost uncertainty analysis includes breaking the project into cost items and probabilistically capturing the uncertainty of each item. Dependencies between these items are important and if not considered properly may influence the accuracy of cost estimation. However these dependencies are seldom examined and there are theoretical and practical obstacles in modeling them. This paper proposes a quantitative assessment framework integrating the inference process of Bayesian networks (BN) to the traditional probabilistic risk analysis. BNs provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. The new approach explicitly quantifies uncertainty in project cost and also provides an appropriate method for modeling complex relationships in a project, such as common causal factors, formal use of experts' judgments, and learning from data to update previous beliefs and probabilities. The capabilities of the proposed approach are explained by a simple example.},\n bibtype = {article},\n author = {Khodakarami, Vahid and Abdi, Abdollah},\n doi = {10.1016/j.ijproman.2014.01.001},\n journal = {International Journal of Project Management},\n number = {7}\n}

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\n \n 2013\n \n \n (11)\n \n \n

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\n \n\n \n \n \n \n \n \n Human reliability modeling for the Next Generation System Code.\n \n \n \n \n\n\n \n Sundaramurthi, R.; and Smidts, C.\n\n\n \n\n\n\n Annals of Nuclear Energy, 52: 137-156. 2 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Human$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Human reliability modeling for the Next Generation System Code},\n type = {article},\n year = {2013},\n keywords = {Bayesian belief network,Human error,Human reliability,IDA,IDAC,Performance shaping factors},\n pages = {137-156},\n volume = {52},\n websites = {http://www.sciencedirect.com/science/article/pii/S0306454912003064},\n month = {2},\n id = {258e801e-fdd1-3672-81c0-35a6d368faa1},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper derives the human reliability model requirements for the Next Generation System Code which will be utilized to determine risk-informed safety margins for nuclear power plants through dynamic probabilistic risk analysis. The proposed model is flexible, with the facility to apply a coarse-grain or a fine-grain structure based on the desired resolution level. The varying resolution is achieved by employing human reliability analysis methods with the demonstrated capability of handling human errors that occur during the execution of procedural activities for the coarse-grain structure and the advanced cognitive IDA/IDAC method for the fine-grain structure. The paper proposes improvements to the existing IDA/IDAC model to incorporate functionalities demanded by the NGSC. The improvements are derived for four modules of IDA/IDAC. A Bayesian belief network is constructed for the performance-shaping factors and the conditional probability for existence of each factor is computed from data collected from aviation and nuclear accidents. The influence of the performance-shaping factors on the strategy-selection process of the operator is also depicted. A foundation is laid for the development of mental models with a focus on NPP operation. The research lists the modifications/additions required for the IDA/IDAC method to enable the incorporation of Human Reliability Analysis (HRA) into the Next Generation System Code.},\n bibtype = {article},\n author = {Sundaramurthi, R. and Smidts, C.},\n doi = {10.1016/j.anucene.2012.07.027},\n journal = {Annals of Nuclear Energy}\n}

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\n \n\n \n \n \n \n \n \n Cyber-risk decision models: To insure IT or not?.\n \n \n \n \n\n\n \n Mukhopadhyay, A.; Chatterjee, S.; Saha, D.; Mahanti, A.; and Sadhukhan, S., K.\n\n\n \n\n\n\n Decision Support Systems, 56: 11-26. 12 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Cyber-risk$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Cyber-risk decision models: To insure IT or not?},\n type = {article},\n year = {2013},\n keywords = {Bayesian Belief Network,Copula,Cyber-insurance,Cyber-risk,Premium,Security breach,Utility models},\n pages = {11-26},\n volume = {56},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923613001115},\n month = {12},\n id = {e61e583c-6154-3661-9a92-7d15042717de},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-03-15},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Security breaches adversely impact profit margins, market capitalization and brand image of an organization. Global organizations resort to the use of technological devices to reduce the frequency of a security breach. To minimize the impact of financial losses from security breaches, we advocate the use of cyber-insurance products. This paper proposes models to help firms decide on the utility of cyber-insurance products and to what extent they can use them. In this paper, we propose a Copula-aided Bayesian Belief Network (CBBN) for cyber-vulnerability assessment (C-VA), and expected loss computation. Taking these as an input and using the concepts of collective risk modeling theory, we also compute the premium that a cyber risk insurer can charge to indemnify cyber losses. Further, to assist cyber risk insurers and to effectively design products, we propose a utility based preferential pricing (UBPP) model. UBPP takes into account risk profiles and wealth of the prospective insured firm before proposing the premium.},\n bibtype = {article},\n author = {Mukhopadhyay, Arunabha and Chatterjee, Samir and Saha, Debashis and Mahanti, Ambuj and Sadhukhan, Samir K.},\n doi = {10.1016/j.dss.2013.04.004},\n journal = {Decision Support Systems}\n}

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\n \n\n \n \n \n \n \n \n Transportation mode recognition using GPS and accelerometer data.\n \n \n \n \n\n\n \n Feng, T.; and Timmermans, H., J.\n\n\n \n\n\n\n Transportation Research Part C: Emerging Technologies, 37: 118-130. 12 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Transportation$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Transportation mode recognition using GPS and accelerometer data},\n type = {article},\n year = {2013},\n keywords = {Accelerometer,Activity type,Bayesian Belief Network,GPS,Transportation mode},\n pages = {118-130},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0968090X13002039},\n month = {12},\n id = {32252da2-3a10-3ac8-acf8-1a0ebeb77710},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-03-20},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.},\n bibtype = {article},\n author = {Feng, Tao and Timmermans, Harry J.P.},\n doi = {10.1016/j.trc.2013.09.014},\n journal = {Transportation Research Part C: Emerging Technologies}\n}

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\n \n\n \n \n \n \n \n \n An Integrated Approach for Risk Assessment of CO2 Infrastructure in the COCATE Project.\n \n \n \n \n\n\n \n Kvien, K.; Flach, T.; Solomon, S.; Napoles, O., M.; Hulsbosch-Dam, C.; and Spruijt, M.\n\n\n \n\n\n\n Energy Procedia, 37: 2932-2940. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An Integrated Approach for Risk Assessment of CO2 Infrastructure in the COCATE Project},\n type = {article},\n year = {2013},\n keywords = {Bayesian belief network,CO2 infrastructure,COCATE,Risk assessment,loss-of-containment},\n pages = {2932-2940},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S1876610213004220},\n id = {9354da27-6db4-3677-91fa-9c2d0b0ef3cd},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {An innovative risk analysis model has been developed in order to quantify and analyse safety risks related to loss-of- containment scenarios in the pipeline transport of CO2. The risk model integrates the identified failure modes, consequence estimates and emergency response, producing consistent risk profiles based on complete outcome spaces and for different system design choices. The method involves integration in a Bayesian Belief Network (BN) of analytical equations for gas dispersion combined with statistics and expert estimates of particularly uncertain variables. Future failure initiators, scenarios and impacts are captured in a graphical structure which represents and calculates the effects of common causes. The test case for the integrated risk model will be a large CO2 capture and transport network at the Le Havre industrial zone with export to Rotterdam. The primary relative advantages of the BN risk model approach are discussed.},\n bibtype = {article},\n author = {Kvien, Knut and Flach, Todd and Solomon, Semere and Napoles, Oswaldo Morales and Hulsbosch-Dam, Corina and Spruijt, Mark},\n doi = {10.1016/j.egypro.2013.06.179},\n journal = {Energy Procedia}\n}

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\n \n\n \n \n \n \n \n \n Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents.\n \n \n \n \n\n\n \n Martins, M., R.; and Maturana, M., C.\n\n\n \n\n\n\n Reliability Engineering & System Safety, 110: 89-109. 2 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Application$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents},\n type = {article},\n year = {2013},\n keywords = {Bayesian belief networks (BBNs),Collision,Human reliability analysis (HRA),Oil tanker,Probabilistic risk assessment (PRA)},\n pages = {89-109},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0951832012001883},\n month = {2},\n id = {194826ab-ed87-378a-9248-8f2d0dbf09bf},\n created = {2015-04-11T18:33:36.000Z},\n accessed = {2015-03-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {During the last three decades, several techniques have been developed for the quantitative study of human reliability. In the 1980s, techniques were developed to model systems by means of binary trees, which did not allow for the representation of the context in which human actions occur. Thus, these techniques cannot model the representation of individuals, their interrelationships, and the dynamics of a system. These issues make the improvement of methods for Human Reliability Analysis (HRA) a pressing need. To eliminate or at least attenuate these limitations, some authors have proposed modeling systems using Bayesian Belief Networks (BBNs). The application of these tools is expected to address many of the deficiencies in current approaches to modeling human actions with binary trees. This paper presents a methodology based on BBN for analyzing human reliability and applies this method to the operation of an oil tanker, focusing on the risk of collision accidents. The obtained model was used to determine the most likely sequence of hazardous events and thus isolate critical activities in the operation of the ship to study Internal Factors (IFs), Skills, and Management and Organizational Factors (MOFs) that should receive more attention for risk reduction.},\n bibtype = {article},\n author = {Martins, Marcelo Ramos and Maturana, Marcos Coelho},\n doi = {10.1016/j.ress.2012.09.008},\n journal = {Reliability Engineering & System Safety}\n}

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\n \n\n \n \n \n \n \n \n Dynamic decision making for graphical models applied to oil exploration.\n \n \n \n \n\n\n \n Martinelli, G.; Eidsvik, J.; and Hauge, R.\n\n\n \n\n\n\n European Journal of Operational Research, 230(3): 688-702. 11 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Dynamic decision making for graphical models applied to oil exploration},\n type = {article},\n year = {2013},\n keywords = {Bayesian Networks,Dynamic programming,Graphical model,Heuristics,Petroleum exploration},\n pages = {688-702},\n volume = {230},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221713003834},\n month = {11},\n id = {99534cea-47ee-39c2-a2b6-cc62da635462},\n created = {2015-04-11T19:51:55.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy of wells that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from naive and myopic heuristics to more complex look-ahead schemes, and we discuss their computational properties. We apply these strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the naive or myopic constructions used in petroleum industry today. This is useful for decision makers planning petroleum exploration policies.},\n bibtype = {article},\n author = {Martinelli, Gabriele and Eidsvik, Jo and Hauge, Ragnar},\n doi = {10.1016/j.ejor.2013.04.057},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Decision support system for Warfarin therapy management using Bayesian networks.\n \n \n \n \n\n\n \n Yet, B.; Bastani, K.; Raharjo, H.; Lifvergren, S.; Marsh, W.; and Bergman, B.\n\n\n \n\n\n\n Decision Support Systems, 55(2): 488-498. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Decision support system for Warfarin therapy management using Bayesian networks},\n type = {article},\n year = {2013},\n keywords = {Anticoagulant therapy,Bayesian networks,Decision support systems,Warfarin therapy},\n pages = {488-498},\n volume = {55},\n websites = {http://www.sciencedirect.com/science/article/pii/S016792361200262X},\n month = {5},\n id = {f6faa0b0-537a-3915-9af4-4bc62275b49c},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy management. The DSS is developed in collaboration with a Swedish hospital group that manages Warfarin therapy for more than 3000 patients. The proposed model can assist the clinician in making dose-adjustment and follow-up interval decisions, investigating variation causes, and evaluating bleeding and thrombosis risks related to therapy. The model is built upon previous findings from medical literature, the knowledge of domain experts, and large dataset of patients.},\n bibtype = {article},\n author = {Yet, Barbaros and Bastani, Kaveh and Raharjo, Hendry and Lifvergren, Svante and Marsh, William and Bergman, Bo},\n doi = {10.1016/j.dss.2012.10.007},\n journal = {Decision Support Systems},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Software project risk analysis using Bayesian networks with causality constraints.\n \n \n \n \n\n\n \n Hu, Y.; Zhang, X.; Ngai, E.; Cai, R.; and Liu, M.\n\n\n \n\n\n\n Decision Support Systems, 56: 439-449. 12 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Software$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Software project risk analysis using Bayesian networks with causality constraints},\n type = {article},\n year = {2013},\n keywords = {Bayesian networks,Causality analysis,Expert knowledge constraint,Knowledge discovery,Software project risk analysis},\n pages = {439-449},\n volume = {56},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923612003338},\n month = {12},\n id = {1b6eefb1-18d9-32c9-8891-7e04869fa58a},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-02-03},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many risks are involved in software development and risk management has become one of the key activities in software development. Bayesian networks (BNs) have been explored as a tool for various risk management practices, including the risk management of software development projects. However, much of the present research on software risk analysis focuses on finding the correlation between risk factors and project outcome. Software project failures are often a result of insufficient and ineffective risk management. To obtain proper and effective risk control, risk planning should be performed based on risk causality which can provide more risk information for decision making. In this study, we propose a model using BNs with causality constraints (BNCC) for risk analysis of software development projects. Through unrestricted automatic causality learning from 302 collected software project data, we demonstrated that the proposed model can not only discover causalities in accordance with the expert knowledge but also perform better in prediction than other algorithms, such as logistic regression, C4.5, Naïve Bayes, and general BNs. This research presents the first causal discovery framework for risk causality analysis of software projects and develops a model using BNCC for application in software project risk management.},\n bibtype = {article},\n author = {Hu, Yong and Zhang, Xiangzhou and Ngai, E.W.T. and Cai, Ruichu and Liu, Mei},\n doi = {10.1016/j.dss.2012.11.001},\n journal = {Decision Support Systems}\n}

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\n \n\n \n \n \n \n \n \n Bayesian networks make LOPA more effective, QRA more transparent and flexible, and thus safety more definable!.\n \n \n \n \n\n\n \n Pasman, H.; and Rogers, W.\n\n\n \n\n\n\n Journal of Loss Prevention in the Process Industries, 26(3): 434-442. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian networks make LOPA more effective, QRA more transparent and flexible, and thus safety more definable!},\n type = {article},\n year = {2013},\n keywords = {Bayesian networks,Cost–benefit,Process safety,Risk analysis,Software tools},\n pages = {434-442},\n volume = {26},\n websites = {http://www.sciencedirect.com/science/article/pii/S095042301200112X},\n month = {5},\n id = {cf9c96f4-7932-3f99-bd0e-4103fe4ae475},\n created = {2015-04-11T19:52:19.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Quantitative risk analysis is in principle an ideal method to map one’s risks, but it has limitations due to the complexity of models, scarcity of data, remaining uncertainties, and above all because effort, cost, and time requirements are heavy. Also, software is not cheap, the calculations are not quite transparent, and the flexibility to look at various scenarios and at preventive and protective options is limited. So, the method is considered as a last resort for determination of risks. Simpler methods such as LOPA that focus on a particular scenario and assessment of protection for a defined initiating event are more popular. LOPA may however not cover the whole range of credible scenarios, and calamitous surprises may emerge. In the past few decades, Artificial Intelligence university groups, such as the Decision Systems Laboratory of the University of Pittsburgh, have developed Bayesian approaches to support decision making in situations where one has to weigh gains and costs versus risks. This paper will describe details of such an approach and will provide some examples of both discrete random variables, such as the probability values in a LOPA, and continuous distributions, which can better reflect the uncertainty in data.},\n bibtype = {article},\n author = {Pasman, Hans and Rogers, William},\n doi = {10.1016/j.jlp.2012.07.016},\n journal = {Journal of Loss Prevention in the Process Industries},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Quantitative risk analysis of offshore drilling operations: A Bayesian approach.\n \n \n \n \n\n\n \n Khakzad, N.; Khan, F.; and Amyotte, P.\n\n\n \n\n\n\n Safety Science, 57: 108-117. 8 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Quantitative risk analysis of offshore drilling operations: A Bayesian approach},\n type = {article},\n year = {2013},\n keywords = {Blowout,Bow-tie approach,Drilling,Kick,Object-oriented Bayesian network,Risk analysis},\n pages = {108-117},\n volume = {57},\n websites = {http://www.sciencedirect.com/science/article/pii/S0925753513000362},\n month = {8},\n id = {1334394b-2523-3064-8759-6af25a73658e},\n created = {2015-04-11T22:23:06.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Blowouts are among the most undesired and feared accidents during drilling operations. The dynamic nature of blowout accidents, resulting from both rapidly changing physical parameters and time-dependent failure of barriers, necessitates techniques capable of considering time dependencies and changes during the lifetime of a well. The present work is aimed at demonstrating the application of bow-tie and Bayesian network methods in conducting quantitative risk analysis of drilling operations. Considering the former method, fault trees and an event tree are developed for potential accident scenarios, and then combined to build a bow-tie model. In the latter method, first, individual Bayesian networks are developed for the accident scenarios and finally, an object-oriented Bayesian network is constructed by connecting these individual networks. The Bayesian network method provides greater value than the bow-tie model since it can consider common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors.},\n bibtype = {article},\n author = {Khakzad, Nima and Khan, Faisal and Amyotte, Paul},\n doi = {10.1016/j.ssci.2013.01.022},\n journal = {Safety Science}\n}

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\n \n\n \n \n \n \n \n \n Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis.\n \n \n \n \n\n\n \n Karvetski, C., W.; Olson, K., C.; Gantz, D., T.; and Cross, G., A.\n\n\n \n\n\n\n EURO Journal on Decision Processes, 1(3-4): 205-231. 11 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Structuring$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

\n

@article{\n title = {Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis},\n type = {article},\n year = {2013},\n pages = {205-231},\n volume = {1},\n websites = {http://link.springer.com/10.1007/s40070-013-0001-x},\n month = {11},\n publisher = {Springer Berlin Heidelberg},\n day = {30},\n id = {845b6a1c-024b-3f0f-b46b-9d5f2743e303},\n created = {2018-09-17T00:09:58.082Z},\n accessed = {2018-08-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2018-09-17T00:09:58.082Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Karvetski, Christopher W. and Olson, Kenneth C. and Gantz, Donald T. and Cross, Glenn A.},\n doi = {10.1007/s40070-013-0001-x},\n journal = {EURO Journal on Decision Processes},\n number = {3-4}\n}

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\n \n 2012\n \n \n (4)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Slip and fall event detection using Bayesian Belief Network.\n \n \n \n \n\n\n \n Liao, Y., T.; Huang, C.; and Hsu, S.\n\n\n \n\n\n\n Pattern Recognition, 45(1): 24-32. 1 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Slip$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Slip and fall event detection using Bayesian Belief Network},\n type = {article},\n year = {2012},\n keywords = {Bayesian Belief Network (BBN),Integrated spatiotemporal energy (ISTE) map,Motion active (MA) area,Motion history image (MHI),Slip and fall event detection},\n pages = {24-32},\n volume = {45},\n websites = {http://www.sciencedirect.com/science/article/pii/S0031320311001762},\n month = {1},\n id = {34fa0043-754f-3dbf-87d9-82a643507200},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper proposes a method to detect slip-only events and fall events based on the motion activity measure and human silhouette shape variations. Here, we also apply the Bayesian Belief Network (BBN) to model the causality of the events before and after the fall and slip-only events. The motion measure is obtained by analyzing the energy of the motion active (MA) area in the integrated spatiotemporal energy (ISTE) map. Unlike the motion history image (MHI), the ISTE map can be applied to detect fall and slip-only events. The contributions of this study are: (a) proposing the ISTE map; (b) detecting the fall parallel to the optical axis; (c) application to non-fixed frame rate video; (d) identifying the slip-only event; and (e) using BBN to model the causality of the slip or fall events with other events. Early identification of a slip-only event can help prevent falls and injuries. In the experiments, we demonstrate that our method is effective in detecting both fall and slip-only events.},\n bibtype = {article},\n author = {Liao, Yi Ting and Huang, Chung-Lin and Hsu, Shih-Chung},\n doi = {10.1016/j.patcog.2011.04.017},\n journal = {Pattern Recognition},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Answering queries in hybrid Bayesian networks using importance sampling.\n \n \n \n \n\n\n \n Fernández, A.; Rumí, R.; and Salmerón, A.\n\n\n \n\n\n\n Decision Support Systems, 53(3): 580-590. 6 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Answering$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Answering queries in hybrid Bayesian networks using importance sampling},\n type = {article},\n year = {2012},\n keywords = {6505,68T37,Bayesian networks,Importance sampling,Mixtures of truncated exponentials,Probabilistic reasoning},\n pages = {580-590},\n volume = {53},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923612000942},\n month = {6},\n id = {72b83b08-5f76-352c-8136-a30dc5a5e94a},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper we propose an algorithm for answering queries in hybrid Bayesian networks where the underlying probability distribution is of class MTE (mixture of truncated exponentials). The algorithm is based on importance sampling simulation. We show how, like existing importance sampling algorithms for discrete networks, it is able to provide answers to multiple queries simultaneously using a single sample. The behaviour of the new algorithm is experimentally tested and compared with previous methods existing in the literature.},\n bibtype = {article},\n author = {Fernández, Antonio and Rumí, Rafael and Salmerón, Antonio},\n doi = {10.1016/j.dss.2012.03.007},\n journal = {Decision Support Systems},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Analysis of facility location model using Bayesian Networks.\n \n \n \n \n\n\n \n Dogan, I.\n\n\n \n\n\n\n Expert Systems with Applications, 39(1): 1092-1104. 1 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Analysis of facility location model using Bayesian Networks},\n type = {article},\n year = {2012},\n keywords = {Bayesian Networks,Facility location,Total Cost of Ownership (TCO)},\n pages = {1092-1104},\n volume = {39},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417411010712},\n month = {1},\n id = {b2c82e19-a37d-3513-ae14-82bd182cfc6c},\n created = {2015-04-11T22:23:04.000Z},\n accessed = {2015-02-02},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this study, we propose an integrated approach that combines Bayesian Networks and Total Cost of Ownership (TCO) to address complexities involved in selecting an international facility for a manufacturing plant. Our goal is to efficiently represent uncertain data and ambiguous information, and to unite them to improve the quality of the decisions. Bayesian Networks provide a framework to elicit information from experts, and provide a structure guide to efficient reasoning, even with incomplete knowledge. Our method is presented in a hierarchical structure so that it can be decomposed into the forms of more manageable units. We consider many tangible and intangible facility location criteria, then these criteria are grouped into few numbers of factors. These factors are then combined to form a cost perspective using the essentials of TCO.},\n bibtype = {article},\n author = {Dogan, Ibrahim},\n doi = {10.1016/j.eswa.2011.07.109},\n journal = {Expert Systems with Applications},\n number = {1}\n}

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\n\n\n

\n \n\n \n \n \n \n \n \n Airport Flight Departure Delay Model on Improved BN Structure Learning.\n \n \n \n \n\n\n \n Cao, W.; and Fang, X.\n\n\n \n\n\n\n Physics Procedia, 33: 597-603. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Airport$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Airport Flight Departure Delay Model on Improved BN Structure Learning},\n type = {article},\n year = {2012},\n keywords = {Flight Departure Delay,Genetic Algorithm,High Score Prior Genetic Simulated Annealing Bayes,Simulated Annealing Algorithm},\n pages = {597-603},\n volume = {33},\n websites = {http://www.sciencedirect.com/science/article/pii/S1875389212014228},\n id = {6912e7e6-c09a-348e-81eb-00d2c9794f68},\n created = {2015-04-11T22:23:05.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.},\n bibtype = {article},\n author = {Cao, Weidong and Fang, Xiangnong},\n doi = {10.1016/j.phpro.2012.05.109},\n journal = {Physics Procedia}\n}

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\n\n\n\n\n\n

\n

\n \n 2011\n \n \n (3)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Systematic causal knowledge acquisition using FCM Constructor for product design decision support.\n \n \n \n \n\n\n \n Cheah, W., P.; Kim, Y., S.; Kim, K.; and Yang, H.\n\n\n \n\n\n\n Expert Systems with Applications, 38(12): 15316-15331. 11 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Systematic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Systematic causal knowledge acquisition using FCM Constructor for product design decision support},\n type = {article},\n year = {2011},\n keywords = {Bayesian belief network,Causal reasoning,Fuzzy cognitive map,Knowledge acquisition,Product design knowledge},\n pages = {15316-15331},\n volume = {38},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417411009274},\n month = {11},\n id = {29ddb365-3d67-3690-8011-99cc1d283688},\n created = {2015-04-11T18:13:54.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application—FCM Constructor—to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system’s usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs.},\n bibtype = {article},\n author = {Cheah, Wooi Ping and Kim, Yun Seon and Kim, Kyoung-Yun and Yang, Hyung-Jeong},\n doi = {10.1016/j.eswa.2011.06.032},\n journal = {Expert Systems with Applications},\n number = {12}\n}

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\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Simulation metamodeling with dynamic Bayesian networks.\n \n \n \n \n\n\n \n Poropudas, J.; and Virtanen, K.\n\n\n \n\n\n\n European Journal of Operational Research, 214(3): 644-655. 11 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Simulation$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Simulation metamodeling with dynamic Bayesian networks},\n type = {article},\n year = {2011},\n keywords = {Discrete event simulation,Dynamic Bayesian networks,Simulation,Simulation metamodeling},\n pages = {644-655},\n volume = {214},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221711004127},\n month = {11},\n id = {73baa809-11ce-376a-b3db-167dfa5a9a07},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a novel approach to simulation metamodeling using dynamic Bayesian networks (DBNs) in the context of discrete event simulation. A DBN is a probabilistic model that represents the joint distribution of a sequence of random variables and enables the efficient calculation of their marginal and conditional distributions. In this paper, the construction of a DBN based on simulation data and its utilization in simulation analyses are presented. The DBN metamodel allows the study of the time evolution of simulation by tracking the probability distribution of the simulation state over the duration of the simulation. This feature is unprecedented among existing simulation metamodels. The DBN metamodel also enables effective what-if analysis which reveals the conditional evolution of the simulation. In such an analysis, the simulation state at a given time is fixed and the probability distributions representing the state at other time instants are updated. Simulation parameters can be included in the DBN metamodel as external random variables. Then, the DBN offers a way to study the effects of parameter values and their uncertainty on the evolution of the simulation. The accuracy of the analyses allowed by DBNs is studied by constructing appropriate confidence intervals. These analyses could be conducted based on raw simulation data but the use of DBNs reduces the duration of repetitive analyses and is expedited by available Bayesian network software. The construction and analysis capabilities of DBN metamodels are illustrated with two example simulation studies.},\n bibtype = {article},\n author = {Poropudas, Jirka and Virtanen, Kai},\n doi = {10.1016/j.ejor.2011.05.007},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n\n\n

\n \n\n \n \n \n \n \n \n Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems.\n \n \n \n \n\n\n \n Shah, R.; and Reed, P.\n\n\n \n\n\n\n European Journal of Operational Research, 211(3): 466-479. 6 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparative$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems},\n type = {article},\n year = {2011},\n keywords = {Combinatorial optimization,Hierarchical Bayesian networks,Knapsack problem,Multiobjective optimization,Probabilistic model building evolutionary algorith},\n pages = {466-479},\n volume = {211},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221711000841},\n month = {6},\n id = {6f6e29b8-e631-3d66-932d-4e96b5881160},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This study analyzes multiobjective d-dimensional knapsack problems (MOd-KP) within a comparative analysis of three multiobjective evolutionary algorithms (MOEAs): the ε-nondominated sorted genetic algorithm II (ε-NSGAII), the strength Pareto evolutionary algorithm 2 (SPEA2) and the ε-nondominated hierarchical Bayesian optimization algorithm (ε-hBOA). This study contributes new insights into the challenges posed by correlated instances of the MOd-KP that better capture the decision interdependencies often present in real world applications. A statistical performance analysis of the algorithms uses the unary ε-indicator, the hypervolume indicator and success rate plots to demonstrate their relative effectiveness, efficiency, and reliability for the MOd-KP instances analyzed. Our results indicate that the ε-hBOA achieves superior performance relative to ε-NSGAII and SPEA2 with increasing number of objectives, number of decisions, and correlative linkages between the two. Performance of the ε-hBOA suggests that probabilistic model building evolutionary algorithms have significant promise for expanding the size and scope of challenging multiobjective problems that can be explored.},\n bibtype = {article},\n author = {Shah, Ruchit and Reed, Patrick},\n doi = {10.1016/j.ejor.2011.01.030},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n 2010\n \n \n (8)\n \n \n

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\n \n\n \n \n \n \n \n \n Assessing critical success factors for military decision support.\n \n \n \n \n\n\n \n Louvieris, P.; Gregoriades, A.; and Garn, W.\n\n\n \n\n\n\n Expert Systems with Applications, 37(12): 8229-8241. 12 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Assessing critical success factors for military decision support},\n type = {article},\n year = {2010},\n keywords = {Bayesian belief networks,Case-based reasoning,Critical success factors,Decision making},\n pages = {8229-8241},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417410004732},\n month = {12},\n id = {e3451e0a-155b-3fd6-ba4f-07ca367ac36e},\n created = {2015-04-11T18:13:54.000Z},\n accessed = {2015-02-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper outlines the application of case-based reasoning and Bayesian belief networks to critical success factor (CSF) assessment for parsimonious military decision making. An important factor for successful military missions is information superiority (IS). However, IS is not solely about minimising information related needs to avoid information overload and the reduction of bandwidth but it is also concerned with creating information related capabilities that are aligned with achieving operational effects and raising operational tempo. Moreover, good military decision making, should take into account the uncertainty inherent in operational situations. Herein, we illustrate the development and evaluation of a smart decision support system (SDSS) that dynamically identifies and assesses CSFs in military scenarios and as such de-clutters the decision making process. The second contribution of this work is an automated configuration of conditional probability tables from hard data generated from simulations of military operational scenarios using a computer generated forces (CGF) synthetic environment.},\n bibtype = {article},\n author = {Louvieris, Panos and Gregoriades, Andreas and Garn, Wolfgang},\n doi = {10.1016/j.eswa.2010.05.062},\n journal = {Expert Systems with Applications},\n number = {12}\n}

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\n \n\n \n \n \n \n \n \n A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA.\n \n \n \n \n\n\n \n Chiang, T.; and Che, Z.\n\n\n \n\n\n\n Expert Systems with Applications, 37(11): 7408-7418. 11 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA},\n type = {article},\n year = {2010},\n keywords = {Bayesian belief network,Fuzzy AHP,Fuzzy DEA,NPD risk,New product development project},\n pages = {7408-7418},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417410003052},\n month = {11},\n id = {83334cca-801e-31fe-8c8c-b02ffed4e16a},\n created = {2015-04-11T19:07:36.000Z},\n accessed = {2015-03-24},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Due to brutal business competition, new product development (NPD) has become a key factor for promoting business sustainability. To help a company determine the direction of NPD for the future, this study applies the fuzzy analytical hierarchy procedure (AHP) and fuzzy data envelopment analysis (DEA) to develop an evaluation and ranking methodology, assisting decision makers to select NPD projects with development potential and high added value. Because of the high risk characteristic of NPD, this study employs the Bayesian belief network (BBN) technology to create the risk evaluation models to assist the top managers in analyzing and measuring the NPD risks. Finally, this paper employs the development projects of the electronic extension cards as a case study for explanation and verification of significant benefits of the methodology proposed by this study.},\n bibtype = {article},\n author = {Chiang, Tzu-An and Che, Z.H.},\n doi = {10.1016/j.eswa.2010.04.034},\n journal = {Expert Systems with Applications},\n number = {11}\n}

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\n \n\n \n \n \n \n \n \n Assessing critical success factors for military decision support.\n \n \n \n \n\n\n \n Louvieris, P.; Gregoriades, A.; and Garn, W.\n\n\n \n\n\n\n Expert Systems with Applications, 37(12): 8229-8241. 12 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Assessing critical success factors for military decision support},\n type = {article},\n year = {2010},\n keywords = {Bayesian belief networks,Case-based reasoning,Critical success factors,Decision making},\n pages = {8229-8241},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417410004732},\n month = {12},\n id = {60a429d4-ebb3-3309-8345-92160e08ac1d},\n created = {2015-04-11T19:09:08.000Z},\n accessed = {2015-02-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper outlines the application of case-based reasoning and Bayesian belief networks to critical success factor (CSF) assessment for parsimonious military decision making. An important factor for successful military missions is information superiority (IS). However, IS is not solely about minimising information related needs to avoid information overload and the reduction of bandwidth but it is also concerned with creating information related capabilities that are aligned with achieving operational effects and raising operational tempo. Moreover, good military decision making, should take into account the uncertainty inherent in operational situations. Herein, we illustrate the development and evaluation of a smart decision support system (SDSS) that dynamically identifies and assesses CSFs in military scenarios and as such de-clutters the decision making process. The second contribution of this work is an automated configuration of conditional probability tables from hard data generated from simulations of military operational scenarios using a computer generated forces (CGF) synthetic environment.},\n bibtype = {article},\n author = {Louvieris, Panos and Gregoriades, Andreas and Garn, Wolfgang},\n doi = {10.1016/j.eswa.2010.05.062},\n journal = {Expert Systems with Applications},\n number = {12}\n}

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\n \n\n \n \n \n \n \n \n A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants.\n \n \n \n \n\n\n \n Häger, D.; and Andersen, L., B.\n\n\n \n\n\n\n European Journal of Operational Research, 207(3): 1635-1644. 12 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants},\n type = {article},\n year = {2010},\n keywords = {Advanced measurement approach,Bayesian networks,Loss determinants,OR in financial institutions,Risk management},\n pages = {1635-1644},\n volume = {207},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221710004261},\n month = {12},\n id = {7c57de88-7728-3b1b-b322-d2066cb56265},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Modelling loss severity from rare operational risk events with potentially catastrophic consequences has proved a difficult task for practitioners in the finance industry. Efforts to develop loss severity models that comply with the BASEL II Capital Accord have resulted in two principal model directions where one is based on scenario generated data and the other on scaling of pooled external data. However, lack of relevant historical data and difficulties in constructing relevant scenarios frequently raise questions regarding the credibility of the resulting loss predictions. In this paper we suggest a knowledge based approach for establishing severity distributions based on loss determinants and their causal influence. Loss determinants are key elements affecting the actual size of potential losses, e.g. market volatility, exposure and equity capital. The loss severity distribution is conditional on the state of the identified loss determinants, thus linking loss severity to underlying causal drivers. We suggest Bayesian Networks as a powerful framework for quantitative analysis of the causal mechanisms determining loss severity. Leaning on available data and expert knowledge, the approach presented in this paper provides improved credibility of the loss predictions without being dependent on extensive data volumes.},\n bibtype = {article},\n author = {Häger, David and Andersen, Lasse B.},\n doi = {10.1016/j.ejor.2010.06.020},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Operations risk management by optimally planning the qualified workforce capacity.\n \n \n \n \n\n\n \n Fragnière, E.; Gondzio, J.; and Yang, X.\n\n\n \n\n\n\n European Journal of Operational Research, 202(2): 518-527. 4 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Operations$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Operations risk management by optimally planning the qualified workforce capacity},\n type = {article},\n year = {2010},\n keywords = {Bayesian network,Endogenous stochastic programming,Manpower planning,Operational risk management},\n pages = {518-527},\n volume = {202},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221709003488},\n month = {4},\n id = {4be21c51-44fa-3ade-9aa7-b161633903ed},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Operational risks are defined as risks of human origin. Unlike financial risks that can be handled in a financial manner (e.g. insurances, savings, derivatives), the treatment of operational risks calls for a “managerial approach”. Consequently, we propose a new way of dealing with operational risk, which relies on the well known aggregate planning model. To illustrate this idea, we have adapted this model to the case of a back office of a bank specializing in the trading of derivative products. Our contribution corresponds to several improvements applied to stochastic programming techniques. First, the model is transformed into a multistage stochastic program in order to take into account the randomness associated with the volume of transaction demand and with the capacity of work provided by qualified and non-qualified employees over the planning horizon. Second, as advocated by Basel II, we calculate the probability distribution based on a Bayesian Network to circumvent the difficulty of obtaining data which characterizes uncertainty in operations. Third, we go a step further by relaxing the traditional assumption in stochastic programming that imposes a strict independence between the decision variables and the random elements. Comparative results show that in general these improved stochastic programming models tend to allocate more human expertise in order to hedge operational risks. Finally, we employ the dual solutions of the stochastic programs to detect periods and nodes that are at risk in terms of the expertise availability.},\n bibtype = {article},\n author = {Fragnière, Emmanuel and Gondzio, Jacek and Yang, Xi},\n doi = {10.1016/j.ejor.2009.05.026},\n journal = {European Journal of Operational Research},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Modeling challenges with influence diagrams: Constructing probability and utility models.\n \n \n \n \n\n\n \n Bielza, C.; and Shenoy, P.\n\n\n \n\n\n\n Decision Support Systems, 49(4): 354-364. 11 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Modeling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Modeling challenges with influence diagrams: Constructing probability and utility models},\n type = {article},\n year = {2010},\n keywords = {Bayesian networks,Ceteris paribus networks,Decision-making under uncertainty,Expected utility networks,Generalized additive independence networks,Influence diagrams,Probabilistic graphical models,Utility ceteris paribus networks,Utility diagrams},\n pages = {354-364},\n volume = {49},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923610000710},\n month = {11},\n id = {e852398f-5d07-37ff-87bf-623a2ddb9869},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Influence diagrams have become a popular tool for representing and solving complex decision-making problems under uncertainty. In this paper, we focus on the task of building probability models from expert knowledge, and also on the challenging and less known task of constructing utility models in influence diagrams. Our goal is to review the state of the art and list some challenges. Similarly to probability models, which are embedded in influence diagrams as a Bayesian network, preferential/utility independence conditions can be used to factor the joint utility function into small factors and reduce the number of parameters needed to fully define the joint function. A number of graphical models have been recently proposed to factor the joint utility function, including the generalized additive independence networks, ceteris paribus networks, utility ceteris paribus networks, expected utility networks, and utility diagrams. Similarly to probability models, utility models can also be engineered from a domain expert or induced from data.},\n bibtype = {article},\n author = {Bielza, C. and Shenoy, P.P.},\n doi = {10.1016/j.dss.2010.04.003},\n journal = {Decision Support Systems},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian Networks approach to Operational Risk.\n \n \n \n \n\n\n \n Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; and Ferri, G.\n\n\n \n\n\n\n Physica A: Statistical Mechanics and its Applications, 389(8): 1721-1728. 4 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Networks approach to Operational Risk},\n type = {article},\n year = {2010},\n keywords = {Bayesian Networks,Complex systems,Different-times correlations,Operational Risk,Time series,Value-at-risk},\n pages = {1721-1728},\n volume = {389},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378437109010577},\n month = {4},\n id = {2aa53026-ab02-3317-9c9c-6ef7c0534416},\n created = {2015-04-11T22:23:04.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.},\n bibtype = {article},\n author = {Aquaro, V. and Bardoscia, M. and Bellotti, R. and Consiglio, A. and De Carlo, F. and Ferri, G.},\n doi = {10.1016/j.physa.2009.12.043},\n journal = {Physica A: Statistical Mechanics and its Applications},\n number = {8}\n}

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\n \n\n \n \n \n \n \n \n Linking Bayesian networks and PLS path modeling for causal analysis.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n Expert Systems with Applications, 37(1): 134-139. 1 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Linking Bayesian networks and PLS path modeling for causal analysis},\n type = {article},\n year = {2010},\n pages = {134-139},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417409004382},\n month = {1},\n id = {227630bd-0c0d-3198-ac95-7fdba62940d5},\n created = {2015-10-19T01:37:00.000Z},\n accessed = {2014-12-13},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed.},\n bibtype = {article},\n author = {},\n journal = {Expert Systems with Applications},\n number = {1}\n}

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\n \n 2009\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n A linear Bayesian stochastic approximation to update project duration estimates.\n \n \n \n \n\n\n \n Cho, S.\n\n\n \n\n\n\n European Journal of Operational Research, 196(2): 585-593. 7 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A linear Bayesian stochastic approximation to update project duration estimates},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief network,Linear Bayesian method,Project duration estimation,Stochastic approximation},\n pages = {585-593},\n volume = {196},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221708003792},\n month = {7},\n id = {4eeab43e-519b-3b07-8d14-876e123fa0e3},\n created = {2015-04-11T18:33:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {By relaxing the unrealistic assumption of probabilistic independence on activity durations in a project, this paper develops a hierarchical linear Bayesian estimation model. Statistical dependence is established between activity duration and the amount of resource, as well as between the amount of resource and the risk factor. Upon observation or assessment of the amount of resource required for an activity in near completion, the posterior expectation and variance of the risk factor can be directly obtained in the Bayesian scheme. Then, the expected amount of resources required for and the expected duration of upcoming activities can be predicted. We simulate an application project in which the proposed model tracks the varying critical path activities on a real time basis, and updates the expected project duration throughout the entire project. In the analysis, the proposed model improves the prediction accuracy by 38.36% compared to the basic PERT approach.},\n bibtype = {article},\n author = {Cho, Sungbin},\n doi = {10.1016/j.ejor.2008.04.019},\n journal = {European Journal of Operational Research},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization.\n \n \n \n \n\n\n \n Mohaghegh, Z.; Kazemi, R.; and Mosleh, A.\n\n\n \n\n\n\n Reliability Engineering & System Safety, 94(5): 1000-1018. 5 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Incorporating$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization},\n type = {article},\n year = {2009},\n keywords = {Bayesian Belief Network (BBN),Human Reliability Analysis (HRA),Organizational factors,Probabilistic Risk Assessment (PRA),Safety culture,Safety management,Socio-technical complex systems,System dynamics},\n pages = {1000-1018},\n volume = {94},\n websites = {http://www.sciencedirect.com/science/article/pii/S095183200800269X},\n month = {5},\n id = {5120fb81-0ffe-38a1-89ce-4beec0ce7e39},\n created = {2015-04-11T18:46:33.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper is a result of a research with the primary purpose of extending Probabilistic Risk Assessment (PRA) modeling frameworks to include the effects of organizational factors as the deeper, more fundamental causes of accidents and incidents. There have been significant improvements in the sophistication of quantitative methods of safety and risk assessment, but the progress on techniques most suitable for organizational safety risk frameworks has been limited. The focus of this paper is on the choice of “representational schemes” and “techniques.” A methodology for selecting appropriate candidate techniques and their integration in the form of a “hybrid” approach is proposed. Then an example is given through an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT) in order to demonstrate the feasibility and value of hybrid techniques. The proposed hybrid approach integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool for complex socio-technical systems. An application of the hybrid technique is provided in the aviation safety domain, focusing on airline maintenance systems. The example demonstrates how the hybrid method can be used to analyze the dynamic effects of organizational factors on system risk.},\n bibtype = {article},\n author = {Mohaghegh, Zahra and Kazemi, Reza and Mosleh, Ali},\n doi = {10.1016/j.ress.2008.11.006},\n journal = {Reliability Engineering & System Safety},\n number = {5}\n}

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\n \n\n \n \n \n \n \n \n Large engineering project risk management using a Bayesian belief network.\n \n \n \n \n\n\n \n Lee, E.; Park, Y.; and Shin, J., G.\n\n\n \n\n\n\n Expert Systems with Applications, 36(3): 5880-5887. 4 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Large$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Large engineering project risk management using a Bayesian belief network},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief network,Risk management in large engineering projects,Shipbuilding industry},\n pages = {5880-5887},\n volume = {36},\n websites = {http://www.sciencedirect.com/science/article/pii/S095741740800448X},\n month = {4},\n id = {97dce2be-fd4f-3bcd-a819-dee9c398bafe},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-01-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks.},\n bibtype = {article},\n author = {Lee, Eunchang and Park, Yongtae and Shin, Jong Gye},\n doi = {10.1016/j.eswa.2008.07.057},\n journal = {Expert Systems with Applications},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Quantifying schedule risk in construction projects using Bayesian belief networks.\n \n \n \n \n\n\n \n Luu, V., T.; Kim, S.; Tuan, N., V.; and Ogunlana, S., O.\n\n\n \n\n\n\n International Journal of Project Management, 27(1): 39-50. 1 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Quantifying schedule risk in construction projects using Bayesian belief networks},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief networks,Construction projects,Delays,Risk management,Scheduling,Vietnam},\n pages = {39-50},\n volume = {27},\n websites = {http://www.sciencedirect.com/science/article/pii/S0263786308000380},\n month = {1},\n id = {c017b84f-fccc-37fe-b3f4-932db3e224a7},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-03-02},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Delays on construction projects cause financial losses for project stakeholders in developing countries. This paper describes how Bayesian belief network (BBN) is applied to quantify the probability of construction project delays in a developing country. Sixteen factors were identified through a questionnaire survey of 166 professionals. Eighteen cause-effect relationships among these factors were obtained through expert interview survey to develop a belief network model. The validity of the proposed model is tested using two realistic case studies. The findings of the study revealed that financial difficulties of owners and contractors, contractor’s inadequate experience, and shortage of materials are the main causes of delay on construction projects in Vietnam. The results encourage practitioners to benefit from the BBNs. This approach is general and, as such, it may be applied to other construction projects with minor modifications.},\n bibtype = {article},\n author = {Luu, Van Truong and Kim, Soo-Yong and Tuan, Nguyen Van and Ogunlana, Stephen O.},\n doi = {10.1016/j.ijproman.2008.03.003},\n journal = {International Journal of Project Management},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n Decision Support Systems, 48(1): 180-190. 12 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Histogram$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach},\n type = {article},\n year = {2009},\n pages = {180-190},\n volume = {48},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923609001742},\n month = {12},\n id = {21756f7b-052e-32ee-9ea7-f2e4f88debd2},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this work we introduce a methodology based on histogram distances for the automatic induction of Bayesian Networks (BN) from a file containing cases and variables related to a supervised classification problem. The main idea consists of learning the Bayesian Network structure for classification purposes taking into account the classification itself, by comparing the class distribution histogram distances obtained by the Bayesian Network after classifying each case. The structure is learned by applying eight different measures or metrics: the Cooper and Herskovits metric for a general Bayesian Network and seven different statistical distances between pairs of histograms. The results obtained confirm the hypothesis of the authors about the convenience of having a BN structure learning method which takes into account the existence of the special variable (the one corresponding to the class) in supervised classification problems.},\n bibtype = {article},\n author = {},\n journal = {Decision Support Systems},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs.\n \n \n \n \n\n\n \n Jensen, K., L.; Toftum, J.; and Friis-Hansen, P.\n\n\n \n\n\n\n Building and Environment, 44(3): 456-462. 3 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs},\n type = {article},\n year = {2009},\n keywords = {Bayesian Network,Indoor Climate,Performance,Temperature,Total building economics},\n pages = {456-462},\n volume = {44},\n websites = {http://www.sciencedirect.com/science/article/pii/S0360132308000693},\n month = {3},\n id = {91fcb523-674e-3f04-8990-a87184f95ca0},\n created = {2015-04-11T20:33:12.000Z},\n accessed = {2015-04-03},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A Bayesian Network approach has been developed that can compare different building designs by estimating the effects of the thermal indoor environment on the mental performance of office workers. A part of this network is based on the compilation of subjective thermal sensation data and the associated objective thermal measurements from 12,000 office occupants from different parts of the world. A Performance Index (Π) is introduced that can be used to compare directly the different building designs and furthermore to assess the total economic consequences of the indoor climate with a specific building design. In this paper, focus will be on the effects of temperature on mental performance and not on other indoor climate factors. A total economic comparison of six different building designs, four located in northern Europe and two in Los Angeles, USA, was performed. The results indicate that investments in improved indoor thermal conditions can be justified economically in most cases. The Bayesian Network provides a reliable platform using probabilities for modelling the complexity while estimating the effect of indoor climate factors on human beings, due to the different ways in which humans are affected by the indoor climate.},\n bibtype = {article},\n author = {Jensen, Kasper L. and Toftum, Jørn and Friis-Hansen, Peter},\n doi = {10.1016/j.buildenv.2008.04.008},\n journal = {Building and Environment},\n number = {3}\n}

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\n \n 2008\n \n \n (8)\n \n \n

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\n \n\n \n \n \n \n \n \n Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks.\n \n \n \n \n\n\n \n Bashari, H.; Smith, C.; and Bosch, O.\n\n\n \n\n\n\n Agricultural Systems, 99(1): 23-34. 12 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Developing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks},\n type = {article},\n year = {2008},\n keywords = {Adaptive management,Bayesian belief network,Decision support,Queensland,Rangeland management,State and transition model},\n pages = {23-34},\n volume = {99},\n websites = {http://www.sciencedirect.com/science/article/pii/S0308521X08000966},\n month = {12},\n id = {c9af9af2-5c0d-3f91-bd84-3222d065a967},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-04-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {State and transition models provide a simple and versatile way of describing vegetation dynamics in rangelands. However, state and transition models are traditionally descriptive, which has limited their practical application to rangeland management decision support. This paper demonstrates an approach to rangeland management decision support that combines a state and transition model with a Bayesian belief network to provide a relatively simple and updatable rangeland dynamics model that can accommodate uncertainty and be used for scenario, diagnostic, and sensitivity analysis. A state and transition model, developed by the authors for subtropical grassland in south-east Queensland, Australia, is used as an example. From the state and transition model, an influence diagram was built to show the possible transitions among states and the factors influencing each transition. The influence diagram was populated with probabilities to produce a predictive model in the form of a Bayesian belief network. The behaviour of the model was tested using scenario and sensitivity analysis, revealing that selective grazing, grazing pressure, and soil nutrition were believed to influence most transitions, while fire frequency and the frequency of good wet seasons were also important in some transitions. Overall, the integration of a state and transition model with a Bayesian belief network provided a useful way to utilise the knowledge embedded in a state and transition model for predictive purposes. Using a Bayesian belief network in the modelling approach allowed uncertainty and variability to be explicitly accommodated in the modelling process, and expert knowledge to be utilised in model development. The methods used also supported learning from monitoring data, thereby supporting adaptive rangeland management.},\n bibtype = {article},\n author = {Bashari, H. and Smith, C. and Bosch, O.J.H.},\n doi = {10.1016/j.agsy.2008.09.003},\n journal = {Agricultural Systems},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation.\n \n \n \n \n\n\n \n Trucco, P.; Cagno, E.; Ruggeri, F.; and Grande, O.\n\n\n \n\n\n\n Reliability Engineering & System Safety, 93(6): 845-856. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation},\n type = {article},\n year = {2008},\n keywords = {Bayesian Belief Network,Human and organisational factors,Maritime industry,Risk analysis},\n pages = {845-856},\n volume = {93},\n websites = {http://www.sciencedirect.com/science/article/pii/S0951832007001214},\n month = {6},\n id = {eba3a6ad-5e0e-3468-9865-a5fb9b495f68},\n created = {2015-04-11T18:33:36.000Z},\n accessed = {2015-03-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts’ judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC.},\n bibtype = {article},\n author = {Trucco, P. and Cagno, E. and Ruggeri, F. and Grande, O.},\n doi = {10.1016/j.ress.2007.03.035},\n journal = {Reliability Engineering & System Safety},\n number = {6}\n}

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\n \n\n \n \n \n \n \n \n A socio-technical approach to business process simulation.\n \n \n \n \n\n\n \n Gregoriades, A.; and Sutcliffe, A.\n\n\n \n\n\n\n Decision Support Systems, 45(4): 1017-1030. 11 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A socio-technical approach to business process simulation},\n type = {article},\n year = {2008},\n keywords = {Bayesian Belief Networks,Business process simulation,Human performance},\n pages = {1017-1030},\n volume = {45},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923608000766},\n month = {11},\n id = {55ef10ef-a138-333b-80f5-ac16e3960f48},\n created = {2015-04-11T18:46:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper describes a socio-technical approach to business process redesign through the investigation of complex interactions and dependencies among humans and IT systems of organisations. The focus is on the need to assess business process performance early in the redesign process, to prevent organisational failures. The method is based on human performance quantification and is supported by a tool that enables scenario-based evaluation of prospective organisational processes through simulation. The approach combines probabilistic and subjective measures of tasks and communication acts in business processes to quantify business performance in terms of cycle time. The approach models business processes as a set of scenarios of sequential activities where the dependencies between actors, IT systems and tasks are explicitly defined. Business process performance assessment is achieved through a systematic walkthrough of the process model using these scenarios. Human performance constitutes an important parameter to business process performance and is modelled based on Human Performance Shaping Factors (PSF) and assessed using Bayesian Belief Networks (BBN). Process cycle time is calculated using aggregates of task and communication completion times, and calibrated using performance estimates of each of the agents in the scenario. The method enables trade-off analysis among candidate process models and identification of performance bottlenecks early in the design phase. A radiology process improvement case study is presented that demonstrates the use of the method and the tool.},\n bibtype = {article},\n author = {Gregoriades, Andreas and Sutcliffe, Alistair},\n doi = {10.1016/j.dss.2008.04.003},\n journal = {Decision Support Systems},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities.\n \n \n \n \n\n\n \n Gupta, S.; and Kim, H., W.\n\n\n \n\n\n\n European Journal of Operational Research, 190(3): 818-833. 11 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Linking$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities},\n type = {article},\n year = {2008},\n keywords = {Bayesian networks,Customer retention,Decision support,Structural equation modeling,Virtual community},\n pages = {818-833},\n volume = {190},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221707005590},\n month = {11},\n id = {16a809dd-75da-3a79-9f09-474975a6e6bf},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis.},\n bibtype = {article},\n author = {Gupta, Sumeet and Kim, Hee W.},\n doi = {10.1016/j.ejor.2007.05.054},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n Decision Support Systems, 45(2): 368-383. 5 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Learning$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm},\n type = {article},\n year = {2008},\n pages = {368-383},\n volume = {45},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923608000171},\n month = {5},\n id = {6d5b729a-5fce-3f0c-bf65-ddd14ea79c59},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation Maximization (EM) algorithm. A data completing procedure is presented for learning and evaluating the candidate networks. Moreover, a strategy is introduced to obtain better initial networks to facilitate the method. The new method can also overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the databases generated from several benchmark networks illustrate that the new method has better performance than some state-of-the-art algorithms. We also apply the method to a data mining problem and compare the performance of the discovered Bayesian networks with the models generated by other learning algorithms. The results demonstrate that our method outperforms other algorithms.},\n bibtype = {article},\n author = {},\n journal = {Decision Support Systems},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications.\n \n \n \n \n\n\n \n Xu, D., J.; Liao, S., S.; and Li, Q.\n\n\n \n\n\n\n Decision Support Systems, 44(3): 710-724. 2 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Combining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications},\n type = {article},\n year = {2008},\n keywords = {Bayesian networks,Mobile advertising,Mobile commerce,Personalization,User modeling},\n pages = {710-724},\n volume = {44},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923607001765},\n month = {2},\n id = {b317aa7a-2582-3d9c-af7e-37ee5ce5034a},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We propose a design research approach combining behaviour and engineering techniques to better support user modeling in personalized mobile advertising applications. User modeling is a practical means of enabling personalization; one important method to construct user models is that of Bayesian networks. To identify the Bayesian network structure variables and the prior probabilities, empirical experimentation is adopted and context, content, and user preferences are the salient variables. User data collected from the survey are used to set the prior probabilities for the Bayesian network. Experimental evaluation of the system shows it is effective in improving user attitude toward mobile advertising.},\n bibtype = {article},\n author = {Xu, David Jingjun and Liao, Stephen Shaoyi and Li, Qiudan},\n doi = {10.1016/j.dss.2007.10.002},\n journal = {Decision Support Systems},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Predicting traffic flow using Bayesian networks.\n \n \n \n \n\n\n \n Castillo, E.; Menéndez, J., M.; and Sánchez-Cambronero, S.\n\n\n \n\n\n\n Transportation Research Part B: Methodological, 42(5): 482-509. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Predicting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Predicting traffic flow using Bayesian networks},\n type = {article},\n year = {2008},\n keywords = {Gaussian Bayesian networks,Probability intervals,Traffic data updating},\n pages = {482-509},\n volume = {42},\n websites = {http://www.sciencedirect.com/science/article/pii/S0191261507001300},\n month = {6},\n id = {bc3f85c7-1a56-3840-b21a-1026c663e6e0},\n created = {2015-04-11T22:23:05.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper deals with the problem of predicting traffic flows and updating these predictions when information about OD pairs and/or link flows becomes available. To this end, a Bayesian network is built which is able to take into account the random character of the level of total mean flow and the variability of OD pair flows, together with the random violation of the balance equations for OD pairs and link flows due to extra incoming or exiting traffic at links or to measurement errors. Bayesian networks provide the joint density of all unobserved variables and in particular the corresponding conditional and marginal densities, which allow not only joint predictions, but also probability intervals. The influence of congested traffic can also be taken into consideration by combination of the traffic assignment rules (as SUE, for example) with the Bayesian network model proposed. Some examples illustrate the model and show its practical applicability.},\n bibtype = {article},\n author = {Castillo, Enrique and Menéndez, José María and Sánchez-Cambronero, Santos},\n doi = {10.1016/j.trb.2007.10.003},\n journal = {Transportation Research Part B: Methodological},\n number = {5}\n}

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\n \n\n \n \n \n \n \n Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities.\n \n \n \n\n\n \n Gupta, S.; and Kim, H., W.\n\n\n \n\n\n\n European Journal of Operational Research, 190(3): 818-833. 11 2008.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities},\n type = {article},\n year = {2008},\n keywords = {Bayesian networks,Customer retention,Decision support,Structural equation modeling,Virtual community},\n pages = {818-833},\n volume = {190},\n month = {11},\n day = {1},\n id = {d6be7b26-9b55-35ac-bc2f-528eefa018ba},\n created = {2020-01-06T20:25:45.998Z},\n accessed = {2020-01-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2020-01-06T20:25:45.998Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis. © 2007 Elsevier B.V. All rights reserved.},\n bibtype = {article},\n author = {Gupta, Sumeet and Kim, Hee W.},\n doi = {10.1016/j.ejor.2007.05.054},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n 2007\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n A decision support system to improve the efficiency of resource allocation in healthcare management.\n \n \n \n \n\n\n \n Aktaş, E.; Ülengin, F.; and Önsel Şahin, Ş.\n\n\n \n\n\n\n Socio-Economic Planning Sciences, 41(2): 130-146. 6 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A decision support system to improve the efficiency of resource allocation in healthcare management},\n type = {article},\n year = {2007},\n keywords = {Bayesian belief network,Decision support system,Healthcare management},\n pages = {130-146},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S0038012105000480},\n month = {6},\n id = {f2d8a8d7-2a84-3898-86ff-0a9c5a2e76d5},\n created = {2015-04-11T19:07:34.000Z},\n accessed = {2015-02-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Limitations in healthcare funding require hospitals to find more effective ways to utilize resources. An effective patient management system is critically dependent on the accurate analysis of individual patient outcomes and resource utilization. In the current paper, a management-oriented decision support model is thus proposed to assist health system managers in improving the efficiency of their systems. In the first stage of the model, the key variables affecting system efficiency, as well as their causal relationships, are identified through causal maps. Efficiency is measured by the total time spent in the system. In the second stage, a Bayesian Belief Network (BBN) is employed to represent both the conditional dependencies and uncertainties of the key variables. In the third stage, a sensitivity analysis is performed using a BBN to determine the most critical variable(s) in terms of impact on the system. Finally, strategies to improve system efficiency are proposed. The suggested decision support system is applied to the tomography section in the radiology department of a private hospital in Turkey.},\n bibtype = {article},\n author = {Aktaş, Emel and Ülengin, Füsun and Önsel Şahin, Şule},\n doi = {10.1016/j.seps.2005.10.008},\n journal = {Socio-Economic Planning Sciences},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Using Bayesian networks for bankruptcy prediction: Some methodological issues.\n \n \n \n \n\n\n \n Sun, L.; and Shenoy, P., P.\n\n\n \n\n\n\n European Journal of Operational Research, 180(2): 738-753. 7 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Using Bayesian networks for bankruptcy prediction: Some methodological issues},\n type = {article},\n year = {2007},\n keywords = {Bankruptcy prediction,Bayesian networks,Discretization of continuous variables,Naïve Bayes,Variable selection},\n pages = {738-753},\n volume = {180},\n websites = {http://www.sciencedirect.com/science/article/pii/S037722170600289X},\n month = {7},\n id = {0d3b6907-fd78-380b-9b4f-f95408bcd00c},\n created = {2015-04-11T19:51:55.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This study provides operational guidance for building naïve Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naïve Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naïve Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of first-order variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model’s performance. Our results show that the model’s performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model’s performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction.},\n bibtype = {article},\n author = {Sun, Lili and Shenoy, Prakash P.},\n doi = {10.1016/j.ejor.2006.04.019},\n journal = {European Journal of Operational Research},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n A methodology for developing Bayesian networks: An application to information technology (IT) implementation.\n \n \n \n \n\n\n \n Lauría, E., J.; and Duchessi, P., J.\n\n\n \n\n\n\n European Journal of Operational Research, 179(1): 234-252. 5 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A methodology for developing Bayesian networks: An application to information technology (IT) implementation},\n type = {article},\n year = {2007},\n keywords = {Artificial intelligence,Bayesian networks,Decision support systems,IT implementation},\n pages = {234-252},\n volume = {179},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221706000622},\n month = {5},\n id = {4005a38a-98c6-30cf-9d0a-2fcd412c010f},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian Networks (BNs) are probabilistic inference engines that support reasoning under uncertainty. This article presents a methodology for building an information technology (IT) implementation BN from client–server survey data. The article also demonstrates how to use the BN to predict the attainment of IT benefits, given specific implementation characteristics (e.g., application complexity) and activities (e.g., reengineering). The BN is an outcome of a machine learning process that finds the network’s structure and its associated parameters, which best fit the data. The article will be of interest to academicians who want to learn more about building BNs from real data and practitioners who are interested in IT implementation models that make probabilistic statements about certain implementation decisions.},\n bibtype = {article},\n author = {Lauría, Eitel J.M. and Duchessi, Peter J.},\n doi = {10.1016/j.ejor.2006.01.016},\n journal = {European Journal of Operational Research},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Using AI and games for decision support in command and control.\n \n \n \n \n\n\n \n Brynielsson, J.\n\n\n \n\n\n\n Decision Support Systems, 43(4): 1454-1463. 8 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Using AI and games for decision support in command and control},\n type = {article},\n year = {2007},\n keywords = {Bayesian game,Bayesian network,Command and control,Game theory,Influence diagram,Situation awareness},\n pages = {1454-1463},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923606000819},\n month = {8},\n id = {f5ad4ce0-747d-3eb0-bab7-70664b5b45ef},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Developers of tomorrow's command and control centers are facing numerous problems related to the vast amount of available information obtained from various sources. On a lower level, huge amounts of uncertain reports from different sensors need to be fused into comprehensible information. On a higher level, representation and management of the aggregated information will be the main task, with prediction of future course of events being the uttermost goal. Unfortunately, traditional agent modeling techniques do not capture situations where commanders make decisions based on other commanders' reasoning about one's own reasoning. To cope with this problem, we propose a decision support tool for command and control situation awareness enhancements based on game theory for inference and coupled with traditional AI methods for uncertainty modeling.},\n bibtype = {article},\n author = {Brynielsson, Joel},\n doi = {10.1016/j.dss.2006.06.012},\n journal = {Decision Support Systems},\n number = {4}\n}

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\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining.\n \n \n \n \n\n\n \n Huang, Z.; Li, J.; Su, H.; Watts, G., S.; and Chen, H.\n\n\n \n\n\n\n Decision Support Systems, 43(4): 1207-1225. 8 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-scale$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining},\n type = {article},\n year = {2007},\n keywords = {Association rules,Bayesian networks,Genetic regulatory networks,Microarray},\n pages = {1207-1225},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923606000248},\n month = {8},\n id = {c4e7489a-ff97-36db-b19e-84c2b3893584},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-03-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified information-theory-based Bayesian network algorithm and a modified association rule algorithm. Simulation-based evaluation using six datasets indicated that both algorithms outperformed their unmodified counterparts, especially when analyzing large numbers of genes. Both algorithms learned about 20% (50% if directionality and relation type were not considered) of the relations in the actual models. In our empirical evaluation based on two real datasets, domain experts evaluated subsets of learned relations with high confidence and identified 20–30% to be “interesting” or “maybe interesting” as potential experiment hypotheses.},\n bibtype = {article},\n author = {Huang, Zan and Li, Jiexun and Su, Hua and Watts, George S. and Chen, Hsinchun},\n doi = {10.1016/j.dss.2006.02.002},\n journal = {Decision Support Systems},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n Bayesian Networks for enterprise risk assessment.\n \n \n \n \n\n\n \n Bonafede, C.; and Giudici, P.\n\n\n \n\n\n\n Physica A: Statistical Mechanics and its Applications, 382(1): 22-28. 8 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian Networks for enterprise risk assessment},\n type = {article},\n year = {2007},\n keywords = {Bayesian Networks,Enterprise risk assessment,Mutual information},\n pages = {22-28},\n volume = {382},\n websites = {http://www.sciencedirect.com/science/article/pii/S037843710700132X},\n month = {8},\n id = {c80b0912-1caa-35c1-ab11-af6d4ba80c17},\n created = {2015-04-11T22:23:05.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.},\n bibtype = {article},\n author = {Bonafede, C.E. and Giudici, P.},\n doi = {10.1016/j.physa.2007.02.065},\n journal = {Physica A: Statistical Mechanics and its Applications},\n number = {1}\n}

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\n \n 2006\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n A Bayesian Belief Network for IT implementation decision support.\n \n \n \n \n\n\n \n Lauría, E., J.; and Duchessi, P., J.\n\n\n \n\n\n\n Decision Support Systems, 42(3): 1573-1588. 12 2006.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Belief Network for IT implementation decision support},\n type = {article},\n year = {2006},\n keywords = {Bayesian Belief Networks (BBNs),Decision Support Systems (DSSs),Information Technology (IT) implementation},\n pages = {1573-1588},\n volume = {42},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923606000078},\n month = {12},\n id = {dc02c047-760c-30d9-9751-8c4052a19a03},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian Belief Networks (BBNs) are graphical models that provide a compact and simple representation of probabilistic data. BBNs depict the relationships among several variables and include conditional probability distributions that make probabilistic statements about those variables. This paper demonstrates how to create a BBN from real-world data on Information Technology implementations. The paper also displays the resulting BBN and describes how it can be incorporated into a DSS to support “what-if” analyses about Information Technology implementations. The paper combines techniques originating from artificial intelligence, statistics, and computer-based decision making.},\n bibtype = {article},\n author = {Lauría, Eitel J.M. and Duchessi, Peter J.},\n doi = {10.1016/j.dss.2006.01.003},\n journal = {Decision Support Systems},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Integrating Bayesian networks and decision trees in a sequential rule-based transportation model.\n \n \n \n \n\n\n \n Janssens, D.; Wets, G.; Brijs, T.; Vanhoof, K.; Arentze, T.; and Timmermans, H.\n\n\n \n\n\n\n European Journal of Operational Research, 175(1): 16-34. 11 2006.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Integrating Bayesian networks and decision trees in a sequential rule-based transportation model},\n type = {article},\n year = {2006},\n keywords = {Activity-based transportation modelling,BNT classifier,Bayesian networks,Decision trees,Transportation},\n pages = {16-34},\n volume = {175},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221705003437},\n month = {11},\n id = {34b229a9-cc52-3899-8d52-782afca106b3},\n created = {2015-04-11T19:51:55.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.},\n bibtype = {article},\n author = {Janssens, Davy and Wets, Geert and Brijs, Tom and Vanhoof, Koen and Arentze, Theo and Timmermans, Harry},\n doi = {10.1016/j.ejor.2005.03.022},\n journal = {European Journal of Operational Research},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Using Bayesian network analysis to support centre of gravity analysis in military planning.\n \n \n \n \n\n\n \n Falzon, L.\n\n\n \n\n\n\n European Journal of Operational Research, 170(2): 629-643. 4 2006.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Using Bayesian network analysis to support centre of gravity analysis in military planning},\n type = {article},\n year = {2006},\n keywords = {Bayesian networks,Decision analysis,Military,Probabilistic models},\n pages = {629-643},\n volume = {170},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221704005156},\n month = {4},\n id = {c10046eb-82d7-3a3a-806a-fc0cc862f6b2},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Centre of gravity (COG) analysis is an integral and cognitively demanding aspect of military operational planning. It involves identifying the enemy and friendly COG and subsequently determining the critical vulnerabilities that have to be degraded or negated to influence the COG of each side. This paper describes a modelling framework based on the causal relationships among the critical capabilities and requirements for an operation. The framework is subsequently used as a basis for the construction, population and analysis of Bayesian networks to support a rigorous and systematic approach to COG analysis. The importance of this work is that it uses existing planning process concepts to facilitate the construction of comprehensive models in which uncertainties and subjective judgements are clearly represented, thus enabling future re-use and traceability. The visual representation of the COG causal structure helps to clarify thinking and provides a way to record and impart this thinking. Moreover, it gives planners the capability to perform impact analysis, that is, to determine which actions are most likely to achieve a desirable end-state. The paper discusses the methodology, development and implementation of the COG Network Effects Tool (COGNET) suite for model population and model checking as well as impact analysis.},\n bibtype = {article},\n author = {Falzon, Lucia},\n doi = {10.1016/j.ejor.2004.06.028},\n journal = {European Journal of Operational Research},\n number = {2}\n}

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\n \n 2004\n \n \n (6)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n BBN-based software project risk management.\n \n \n \n \n\n\n \n Fan, C.; and Yu, Y.\n\n\n \n\n\n\n Journal of Systems and Software, 73(2): 193-203. 10 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"BBN-based$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {BBN-based software project risk management},\n type = {article},\n year = {2004},\n keywords = {BBN (Bayesian Belief Network),Risk profile,Software project risk management,Uncertainty},\n pages = {193-203},\n volume = {73},\n websites = {http://www.sciencedirect.com/science/article/pii/S0164121203003364},\n month = {10},\n id = {a5aedebf-53ae-3a81-a845-68721af057ce},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents a scheme to incorporate BBNs (Bayesian belief networks) in software project risk management. A theoretical model is defined to provide insights into risk management. Based on these insights, we have developed a BBN-based procedure using a feedback loop to predict potential risks, identify sources of risks, and advise dynamic resource adjustment. This approach facilitates the visibility and repeatability of the decision-making process of risk management. Both analytical and simulated cases are reported.},\n bibtype = {article},\n author = {Fan, Chin-Feng and Yu, Yuan-Chang},\n doi = {10.1016/j.jss.2003.12.032},\n journal = {Journal of Systems and Software},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research.\n \n \n \n \n\n\n \n Henderson, J.; and Burn, R.\n\n\n \n\n\n\n Agricultural Systems, 79(1): 3-15. 1 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Uptake$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research},\n type = {article},\n year = {2004},\n keywords = {Bayesian belief networks,Logic models,Monitoring and evaluation,Research management,Uptake pathways},\n pages = {3-15},\n volume = {79},\n websites = {http://www.sciencedirect.com/science/article/pii/S0308521X03000428},\n month = {1},\n id = {72a9fc51-b24b-3015-bd30-b6467712f655},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.},\n bibtype = {article},\n author = {Henderson, J.S. and Burn, R.W.},\n doi = {10.1016/S0308-521X(03)00042-8},\n journal = {Agricultural Systems},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers.\n \n \n \n \n\n\n \n Baesens, B.; Verstraeten, G.; Van den Poel, D.; Egmont-Petersen, M.; Van Kenhove, P.; and Vanthienen, J.\n\n\n \n\n\n\n European Journal of Operational Research, 156(2): 508-523. 7 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers},\n type = {article},\n year = {2004},\n keywords = {Artificial intelligence,Bayesian network classifiers,CRM,Customer loyalty,Marketing},\n pages = {508-523},\n volume = {156},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221703000432},\n month = {7},\n id = {8bd07aa4-0670-38ce-a6d6-c7def76f2f88},\n created = {2015-04-11T19:51:55.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.},\n bibtype = {article},\n author = {Baesens, Bart and Verstraeten, Geert and Van den Poel, Dirk and Egmont-Petersen, Michael and Van Kenhove, Patrick and Vanthienen, Jan},\n doi = {10.1016/S0377-2217(03)00043-2},\n journal = {European Journal of Operational Research},\n number = {2}\n}

\n

\n\n\n

\n Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.\n

\n\n\n

\n \n\n \n \n \n \n \n \n A causal mapping approach to constructing Bayesian networks.\n \n \n \n \n\n\n \n Nadkarni, S.; and Shenoy, P., P.\n\n\n \n\n\n\n Decision Support Systems, 38(2): 259-281. 11 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A causal mapping approach to constructing Bayesian networks},\n type = {article},\n year = {2004},\n keywords = {Bayesian causal maps,Bayesian networks,Causal maps,Cognitive maps},\n pages = {259-281},\n volume = {38},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923603000952},\n month = {11},\n id = {9d52b917-9be4-30a5-a9b1-a79d63a69c29},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision.},\n bibtype = {article},\n author = {Nadkarni, Sucheta and Shenoy, Prakash P.},\n doi = {10.1016/S0167-9236(03)00095-2},\n journal = {Decision Support Systems},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Data mining of Bayesian networks using cooperative coevolution.\n \n \n \n \n\n\n \n Wong, M., L.; Lee, S., Y.; and Leung, K., S.\n\n\n \n\n\n\n Decision Support Systems, 38(3): 451-472. 12 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Data$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Data mining of Bayesian networks using cooperative coevolution},\n type = {article},\n year = {2004},\n keywords = {Bayesian networks,Cooperative coevolution,Data mining,Evolutionary computation},\n pages = {451-472},\n volume = {38},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923603001155},\n month = {12},\n id = {e8fcf9dc-a054-32bd-84bc-288cd3880e6a},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-08},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper describes a novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data. A Bayesian network is a graphical knowledge representation tool. However, learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel algorithm that combines the characteristics of these approaches to improve learning effectiveness and efficiency. The new learning algorithm consists of the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian networks are generated by a cooperative coevolution genetic algorithm (GA). We conduct a number of experiments and compare the new algorithm with our previous algorithm, Minimum Description Length and Evolutionary Programming (MDLEP), which uses evolutionary programming (EP) for network learning. The results illustrate that the new algorithm has better performance. We apply the algorithm to a large real-world data set and compare the performance of the discovered Bayesian networks with that of the back-propagation neural networks and the logistic regression models. This study illustrates that the algorithm is a promising alternative to other data mining algorithms.},\n bibtype = {article},\n author = {Wong, Man Leung and Lee, Shing Yan and Leung, Kwong Sak},\n doi = {10.1016/S0167-9236(03)00115-5},\n journal = {Decision Support Systems},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Midway Revisited: Detecting Deception by Analysis of Competing Hypothesis.\n \n \n \n \n\n\n \n Stech, F., J.; and Elsaesser, C.\n\n\n \n\n\n\n . 11 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Midway$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Midway Revisited: Detecting Deception by Analysis of Competing Hypothesis},\n type = {article},\n year = {2004},\n keywords = {*DECEPTION,*HYPOTHESES,*MILITARY TACTICS,BAYES THEOREM,COUNTERS,INTELLIGENCE,NAVY,SECOND WORLD WAR.},\n websites = {http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA428173},\n month = {11},\n day = {11},\n id = {06e1afae-f210-344d-ad9b-d1d3685bfe78},\n created = {2015-04-17T18:19:48.000Z},\n accessed = {2015-04-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Stech, Frank J. and Elsaesser, Christopher}\n}

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\n\n\n\n\n\n

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\n \n 2003\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Bayesian network based multiagent system—application in e-marketplace.\n \n \n \n \n\n\n \n Kreng, V.; and Chang, C.\n\n\n \n\n\n\n Computers & Mathematics with Applications, 46(2-3): 429-444. 7 2003.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian network based multiagent system—application in e-marketplace},\n type = {article},\n year = {2003},\n keywords = {Bayesian belief networks,E-marketplace,Multiagent system,Supplier selection},\n pages = {429-444},\n volume = {46},\n websites = {http://www.sciencedirect.com/science/article/pii/S0898122103900368},\n month = {7},\n id = {705f2d85-ff4e-382b-a54c-f8fc70af14e3},\n created = {2015-04-11T18:46:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The concept of e-marketplace has been touted through the extensive use of the Internet. However, the task of filtering the potential supplier base in the e-marketplace is tedious while evaluating all the necessary qualitative and quantitative decision factors. Since the buyers have to evaluate and select suppliers by conveying necessary contingent information among potential suppliers, a superior structure of a multiagent system is constructed in this study to present the characteristics of the e-marketplace. The illustrative examples' results prevail to show that, after communicating among the virtual e-marketplace, the suppliers did know how to adapt their strategies to accommodate buyers' demand. On the other hand, the buyers also know which supplier is the most appropriate for short term as well as long term.},\n bibtype = {article},\n author = {Kreng, V.B. and Chang, Chia-Hua},\n doi = {10.1016/S0898-1221(03)90036-8},\n journal = {Computers & Mathematics with Applications},\n number = {2-3}\n}

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\n \n 2001\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n Making decisions: using Bayesian nets and MCDA.\n \n \n \n \n\n\n \n Fenton, N.; and Neil, M.\n\n\n \n\n\n\n Knowledge-Based Systems, 14(7): 307-325. 11 2001.\n \n\n\n\n
\n\n\n\n \n \n $\"Making$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Making decisions: using Bayesian nets and MCDA},\n type = {article},\n year = {2001},\n keywords = {Analytical hierarchy process,Bayesian belief networks,Multi-criteria decision aid},\n pages = {307-325},\n volume = {14},\n websites = {http://www.sciencedirect.com/science/article/pii/S095070510000071X},\n month = {11},\n id = {096def6e-28d4-3d99-bcba-64bdb44f9879},\n created = {2015-04-11T19:07:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian belief nets (BBNs) have proven to be an extremely powerful technique for reasoning under uncertainty. We have used them in a range of real applications concerned with predicting properties of critical systems. In most of these applications we are interested in a single attribute of the system such as safety or reliability. Although such BBNs provide important support for decision making, in many circumstances we need to make decisions based on multiple criteria. For example, a BBN for predicting the safety of a critical system cannot be used to make a decision about whether or not the system should be deployed. This is because such a decision must be based on criteria other than just safety (cost, politics, and environmental factors being obvious examples). In such situations the BBN must be complemented by other decision making techniques such as those of multi-criteria decision aid (MCDA). In this article we explain the role of BBNs in such decision-making and describe a generic decision-making procedure that uses BBNs and MCDA in a complementary way. The procedure consists of identifying the objective and perspective for the decision problem, as well as the stakeholders.This in turn leads to a set of possible actions,a set of criteria and constraints.We distinguish between, uncertain and certain criteria. The BBN links all the criteria and enables us to calculate a value (within some probability distribution in the case of the uncertain criteria) for each criterion for a given action. This means that we can apply traditional MCDA techniques to combine the values for a given action and then to rank the set of actions. The techniques described are demonstrated by real examples, including a safety assessment example that is being used by a major transportation organisation.},\n bibtype = {article},\n author = {Fenton, N and Neil, M},\n doi = {10.1016/S0950-7051(00)00071-X},\n journal = {Knowledge-Based Systems},\n number = {7}\n}

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\n \n\n \n \n \n \n \n Bayesian network approach to making inferences in causal maps.\n \n \n \n\n\n \n Nadkarni, S.; and Shenoy, P., P.\n\n\n \n\n\n\n European Journal of Operational Research, 128(3): 479-498. 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Bayesian network approach to making inferences in causal maps},\n type = {article},\n year = {2001},\n pages = {479-498},\n volume = {128},\n publisher = {Elsevier Science B.V.},\n id = {455231b6-35bc-392f-b111-bc54fa2c6cef},\n created = {2015-04-11T19:07:34.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The main goal of this paper is to describe a new graphical structure called `Bayesian causal maps' to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert's cognition. It is also a Bayesian network, i.e., a graphical representation of an expert's knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modified to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.},\n bibtype = {article},\n author = {Nadkarni, Sucheta and Shenoy, Prakash P.},\n journal = {European Journal of Operational Research},\n number = {3}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n A Bayesian network approach to making inferences in causal maps.\n \n \n \n \n\n\n \n Nadkarni, S.; and Shenoy, P., P.\n\n\n \n\n\n\n European Journal of Operational Research, 128(3): 479-498. 2 2001.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Bayesian network approach to making inferences in causal maps},\n type = {article},\n year = {2001},\n keywords = {Bayesian causal maps,Bayesian networks,Causal maps,Cognitive maps},\n pages = {479-498},\n volume = {128},\n websites = {http://www.sciencedirect.com/science/article/pii/S0377221799003689},\n month = {2},\n id = {e13c7dc8-f848-315b-ad4b-612626471a25},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The main goal of this paper is to describe a new graphical structure called ‘Bayesian causal maps’ to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert’s cognition. It is also a Bayesian network, i.e., a graphical representation of an expert’s knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modified to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.},\n bibtype = {article},\n author = {Nadkarni, Sucheta and Shenoy, Prakash P},\n doi = {10.1016/S0377-2217(99)00368-9},\n journal = {European Journal of Operational Research},\n number = {3}\n}

\n

\n\n\n\n\n\n

\n

\n \n 2000\n \n \n (1)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n Combining disparate sources of information in the safety assessment of software-based systems.\n \n \n \n \n\n\n \n Dahll, G.\n\n\n \n\n\n\n Nuclear Engineering and Design, 195(3): 307-319. 2 2000.\n \n\n\n\n
\n\n\n\n \n \n $\"Combining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Combining disparate sources of information in the safety assessment of software-based systems},\n type = {article},\n year = {2000},\n keywords = {AI, artificial intelligence,BBN, Bayesian Belief Networks,COTS, Commercial-Off-The-Shelf software,FMECA, Failure Mode, Effect and Criticality Analys,Guidelines, Guidelines for reviewing software in s,NPP, nuclear power plant,REPAC, reactor protection and control system excha,SKI, Swedish nuclear power inspectorate,pdf, probability distribution function},\n pages = {307-319},\n volume = {195},\n websites = {http://www.sciencedirect.com/science/article/pii/S0029549399002137},\n month = {2},\n id = {13f16166-6b61-364a-b31a-952389679cfe},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The main topic of the paper is a discussion on how to combine disparate sources of information in the safety assessment of software-based systems. This is based on experience gained through the licensing process of a programmable system in the Swedish nuclear power plant Ringhals, where a guideline for reviewing software in safety-related systems was applied. One lesson learned from this activity is that the approval of a programmable safety critical system, in particular one which is based on Commercial-Off-The-Shelf software, is based on a combination of disparate sources of information. This combination of information is made in a diagrammatic framework. An emerging methodology to combine information about disparate evidences in a systematic way is based on Bayesian Belief Networks. The objective is to show the link between basic information and the confidence one can have in a system.},\n bibtype = {article},\n author = {Dahll, Gustav},\n doi = {10.1016/S0029-5493(99)00213-7},\n journal = {Nuclear Engineering and Design},\n number = {3}\n}

\n

\n\n\n\n\n\n

\n

\n \n 1999\n \n \n (3)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n A probabilistic model for interactive decision-making.\n \n \n \n \n\n\n \n Reverberi, P.; and Talamo, M.\n\n\n \n\n\n\n Decision Support Systems, 25(4): 289-308. 5 1999.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A probabilistic model for interactive decision-making},\n type = {article},\n year = {1999},\n keywords = {Bayesian belief networks,Decision-making under uncertainty,Information-gathering strategy,Interactive solution procedure,Myopic policy},\n pages = {289-308},\n volume = {25},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923699000135},\n month = {5},\n id = {4ffe4d77-c8ca-39e9-9630-294d027cfe66},\n created = {2015-04-11T18:13:54.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A probabilistic reasoning model is defined where the decision maker (d.m.) is engaged in a sequential information-gathering process facing the trade-off between the reliability of the achieved solution and the associated observation cost. The d.m. is directly involved in the proposed flexible control strategy, which is based on information-theoretic principles. The devised strategy works on a Bayesian belief network that allows the efficient representation and manipulation of the knowledge base relevant to the problem domain. It is shown that this strategy guarantees a constant factor approximate solution with respect to the optimum of the decision problem. Some application examples are also discussed.},\n bibtype = {article},\n author = {Reverberi, Pierfrancesco and Talamo, Maurizio},\n doi = {10.1016/S0167-9236(99)00013-5},\n journal = {Decision Support Systems},\n number = {4}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems.\n \n \n \n \n\n\n \n Kang, C.\n\n\n \n\n\n\n Expert Systems with Applications, 17(1): 21-32. 7 1999.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems},\n type = {article},\n year = {1999},\n keywords = {Bayesian belief network,Nuclear power plants,Operator interface module,Sequential inference algorithm},\n pages = {21-32},\n volume = {17},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417499000184},\n month = {7},\n id = {dfdfecbd-adba-3c10-91e8-458540c6d1a2},\n created = {2015-04-11T18:46:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The work reported here provides a framework of diagnostic advisory system for improved operational availability in complex nuclear power plant systems. The rule-based approach typically used for conventional expert systems is abandoned in this work. This is because of the inability of rule-based approaches to properly model the inherent uncertainties and complexities of the relationships involved in the diagnosis of actual complex engineering systems. Rather, our advisory system employs Bayesian belief network (BBN) as a high-level reasoning tool for incorporating inherent uncertainty for use in probabilistic inference. We demonstrate that a rule-based knowledge representation is simply a special case of a general BBN. First, we outline a sequential algorithm to be used in formulating the BBN-based diagnostic operational advice. Then, a prototype BBN-based representation is encoded explicitly through topological symbols and links between them, oriented in a causal direction. Once new system state related evidence from an associated sensor network is entered into this advisory system, it provides an operational advice concerning how to maintain both operational availability and safety. Based upon the framework presented here, further development of our diagnostic maintenance network, integrating a comprehensive sensor network, can be expected to lead to substantial economic gains.},\n bibtype = {article},\n author = {Kang, C},\n doi = {10.1016/S0957-4174(99)00018-4},\n journal = {Expert Systems with Applications},\n number = {1}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems.\n \n \n \n \n\n\n \n Kang, C.\n\n\n \n\n\n\n Expert Systems with Applications, 17(1): 21-32. 7 1999.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems},\n type = {article},\n year = {1999},\n keywords = {Bayesian belief network,Nuclear power plants,Operator interface module,Sequential inference algorithm},\n pages = {21-32},\n volume = {17},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417499000184},\n month = {7},\n id = {614e95d0-6c32-3a4b-8f53-29f8d6f50468},\n created = {2015-04-11T18:56:31.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The work reported here provides a framework of diagnostic advisory system for improved operational availability in complex nuclear power plant systems. The rule-based approach typically used for conventional expert systems is abandoned in this work. This is because of the inability of rule-based approaches to properly model the inherent uncertainties and complexities of the relationships involved in the diagnosis of actual complex engineering systems. Rather, our advisory system employs Bayesian belief network (BBN) as a high-level reasoning tool for incorporating inherent uncertainty for use in probabilistic inference. We demonstrate that a rule-based knowledge representation is simply a special case of a general BBN. First, we outline a sequential algorithm to be used in formulating the BBN-based diagnostic operational advice. Then, a prototype BBN-based representation is encoded explicitly through topological symbols and links between them, oriented in a causal direction. Once new system state related evidence from an associated sensor network is entered into this advisory system, it provides an operational advice concerning how to maintain both operational availability and safety. Based upon the framework presented here, further development of our diagnostic maintenance network, integrating a comprehensive sensor network, can be expected to lead to substantial economic gains.},\n bibtype = {article},\n author = {Kang, C},\n doi = {10.1016/S0957-4174(99)00018-4},\n journal = {Expert Systems with Applications},\n number = {1}\n}

\n

\n\n\n\n\n\n

\n

\n \n 1998\n \n \n (1)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n A belief network approach to optimization and parameter estimation: application to resource and environmental management.\n \n \n \n \n\n\n \n Vans, O.\n\n\n \n\n\n\n Artificial Intelligence, 101(1-2): 135-163. 5 1998.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A belief network approach to optimization and parameter estimation: application to resource and environmental management},\n type = {article},\n year = {1998},\n keywords = {Bayesian methods,Belief networks,Environmental policies,Hybrid models,Optimization,Parameter estimation,Probabilistic models,Resource management,Water quality},\n pages = {135-163},\n volume = {101},\n websites = {http://www.sciencedirect.com/science/article/pii/S0004370298000101},\n month = {5},\n id = {0936e747-a4fb-39be-85d9-f36339ff1766},\n created = {2015-04-11T18:33:33.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {An approach to use Bayesian belief networks in optimization is presented, with an illustration on resource and environmental management. A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of that model. The divergence between prior and posterior probability distributions at the model components is used as an indication on the inconsistency between model structure, parameter values, and other information used. An iteration scheme was developed to force prior and posterior distributions to become equal. This removes inconsistencies between different sources of information. The scheme can be used in different optimization tasks including parameter estimation and optimization between various policy options. Also multiobjective optimization is possible. The approach is illustrated with an example on cost-effective management of river water quality.},\n bibtype = {article},\n author = {Vans, Olli},\n doi = {10.1016/S0004-3702(98)00010-1},\n journal = {Artificial Intelligence},\n number = {1-2}\n}

\n

\n\n\n\n\n\n

\n

\n \n 1997\n \n \n (2)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n Applications of uncertainty-based mental models in organizational learning: A case study in the Indian automobile industry.\n \n \n \n \n\n\n \n Srinivas, V.; and Shekar, B.\n\n\n \n\n\n\n Accounting, Management and Information Technologies, 7(2): 87-112. 1 1997.\n \n\n\n\n
\n\n\n\n \n \n $\"Applications$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Applications of uncertainty-based mental models in organizational learning: A case study in the Indian automobile industry},\n type = {article},\n year = {1997},\n keywords = {Bayesian belief networks,Belief revision,Cognitive maps,Mental models,Organizational learning,Probabilistic networks,Stochastic simulation},\n pages = {87-112},\n volume = {7},\n websites = {http://www.sciencedirect.com/science/article/pii/S0959802297801646},\n month = {1},\n id = {426f7d60-daea-307e-a8df-510022c47bdf},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-03-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper, we discuss the applicability of qualitative and quantitative reasoning techniques to study the process of Organizational Learning. We have used cognitive maps of a company (for the past five years) taken from the Indian automobile industry to understand the Organizational Learning process. We have conducted stochastic simulation experiment on an uncertainty-based cognitive map (the latest year). We generated scenarios for the future and analysed each scenario with respect to data obtained from the past five-years cognitive maps, in light of the theory on Organizational Learning.},\n bibtype = {article},\n author = {Srinivas, V and Shekar, B.},\n doi = {10.1016/S0959-8022(97)80164-6},\n journal = {Accounting, Management and Information Technologies},\n number = {2}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Decision support for real-time telemarketing operations through Bayesian network learning.\n \n \n \n \n\n\n \n Ahn, J.; and Ezawa, K., J.\n\n\n \n\n\n\n Decision Support Systems, 21(1): 17-27. 9 1997.\n \n\n\n\n
\n\n\n\n \n \n $\"Decision$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Decision support for real-time telemarketing operations through Bayesian network learning},\n type = {article},\n year = {1997},\n keywords = {Bayesian network learning,Decision support system,Influence diagrams,Service operations management,Telecommunication applications},\n pages = {17-27},\n volume = {21},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167923697000092},\n month = {9},\n id = {ac9b1173-11be-3327-8924-cc4c6d5e9941},\n created = {2015-04-11T19:51:58.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many knowledge discovery systems have been developed in diverse areas, but few systems address the use of knowledge in decision problems explicitly. This paper presents a decision support system for real-time telemarketing operations using the information extracted from the Bayesian network learning model. A prototype decision support system was developed for AT&T customer-contact employees to provide a recommendation regarding the promotion of a telephone discount plan. The system integrated a Bayesian network learning model (knowledge discovery process) and decision-making technique (influence diagram) to provide real-time decision support. A Bayesian network learning model was used to predict a probability of the customer's response from the previous promotion/response history. The influence diagram framework was used to integrate the predicted probability with the cost and benefit related to the possible actions. It was demonstrated that decision support by the Bayesian network learning model itself can be misleading. However, by linking the Bayesian network learning model with rigorous decision-making techniques such as influence diagrams, the decision support system developed in this paper was shown to provide an intelligent decision advice.},\n bibtype = {article},\n author = {Ahn, Jae-Hyeon and Ezawa, Kazuo J.},\n doi = {10.1016/S0167-9236(97)00009-2},\n journal = {Decision Support Systems},\n number = {1}\n}

\n

\n\n\n\n\n\n

\n

\n \n 1994\n \n \n (2)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n Knowledge Representation Methods for Dairy Decision Support Systems.\n \n \n \n \n\n\n \n Hogeveen, H.; Varner, M.; Brée, D.; Dill, D.; Noordhuizen-Staseen, E.; and Brand, A.\n\n\n \n\n\n\n Journal of Dairy Science, 77(12): 3704-3715. 12 1994.\n \n\n\n\n
\n\n\n\n \n \n $\"Knowledge$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Knowledge Representation Methods for Dairy Decision Support Systems},\n type = {article},\n year = {1994},\n keywords = {BBN,Bayesian belief network,CCM,KBS,conditional causal model,dairy farm management,decision support systems,knowledge representation,knowledge-based system,knowledge-based systems},\n pages = {3704-3715},\n volume = {77},\n websites = {http://www.sciencedirect.com/science/article/pii/S002203029477315X},\n month = {12},\n id = {1615d8fe-42ec-34c3-b459-4cee019404fe},\n created = {2015-04-11T18:13:54.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Knowledge-based systems are currently being applied for decision support systems for management of dairy farms. An important feature in the development and application of knowledge-based systems is the knowledge representation scheme used. Although many knowledge representation schemes are available in artificial intelligence, the existing dairy farm management applications only use production rules. However, the knowledge required for dairy farm management may require other representation schemes, depending on the type of knowledge involved in the decision-making process. Two classes of knowledge can be distinguished: declarative and procedural (or operational) knowledge. Declarative knowledge is concerned with facts in a domain. Procedural knowledge is knowledge of how to use declarative knowledge. For both types of knowledge, several characteristics can be defined: completeness, certainty, generality, and level. Knowledge representation schemes can be ranked according to their performance on the various knowledge characteristics. Common schemes for knowledge representation and their strengths and weaknesses are described. Different knowledge representation schemes are illustrated for mastitis and reproductive management.},\n bibtype = {article},\n author = {Hogeveen, H. and Varner, M.A. and Brée, D.S. and Dill, D.E. and Noordhuizen-Staseen, E.N. and Brand, A.},\n doi = {10.3168/jds.S0022-0302(94)77315-X},\n journal = {Journal of Dairy Science},\n number = {12}\n}

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\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Bayesian logic.\n \n \n \n \n\n\n \n Andersen, K.; and Hooker, J.\n\n\n \n\n\n\n Decision Support Systems, 11(2): 191-210. 2 1994.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian logic},\n type = {article},\n year = {1994},\n keywords = {Bayesian networks,Probabilistic logic},\n pages = {191-210},\n volume = {11},\n websites = {http://www.sciencedirect.com/science/article/pii/0167923694900310},\n month = {2},\n id = {52a7cd3c-930d-3594-ab42-b8d51171cee7},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We combine probabilistic logic and Bayesian networks to obtain the advantages of each in what we call Bayesian logic. Like probabilistic logic, it is a theoretically grounded way of representing and reasoning with uncertainty that uses only as much probabilistic information as one has, since it permits one to specify probabilities as intervals rather than precise values. Like Bayesian networks, it can capture conditional independence relations, which are probably our richest source of probabilistic knowledge. The inference problem in Bayesian logic can be solved as a nonlinear program (which becomes a linear program in ordinary probabilistic logic). We show that Benders decomposition, applied to the nonlinear program, allows one to use the same column generation methods in Bayesian logic that are now being used to solve inference problems in probabilistic logic. We also show that if the independence conditions are properly represented, the number of nonlinear constraints grows only linearly with the number of nodes in a large class of networks (rather than exponentially, as in the general case).},\n bibtype = {article},\n author = {Andersen, K.A. and Hooker, J.N.},\n doi = {10.1016/0167-9236(94)90031-0},\n journal = {Decision Support Systems},\n number = {2}\n}

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\n \n 1989\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Influence diagrams for Bayesian decision analysis.\n \n \n \n \n\n\n \n Smith, J.\n\n\n \n\n\n\n European Journal of Operational Research, 40(3): 363-376. 6 1989.\n \n\n\n\n
\n\n\n\n \n \n $\"Influence$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Influence diagrams for Bayesian decision analysis},\n type = {article},\n year = {1989},\n keywords = {Bayesian decision analysis,belief networks,bidding problems,conditional independence,directed graphs,influence diagrams,sufficiency},\n pages = {363-376},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/0377221789904293},\n month = {6},\n id = {29d791eb-b4e6-3f84-836b-c98c576b637b},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2017-03-14T14:28:30.967Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Influence diagrams for representing Bayesian decision problems are redefined in a formal way using conditional independence. This makes the graphs somewhat more helpful for exploring the consequences of a clients state beliefs. Some important results about the manipulation of influence diagrams are extended and reviewed as is an algorithm for computing an optimal policy. Two new results about the manipulation of influence diagrams are derived. A novel influence diagram representing a practical decision problem is used to illustrate the methodologies presented in this paper.},\n bibtype = {article},\n author = {Smith, J.Q.},\n doi = {10.1016/0377-2217(89)90429-3},\n journal = {European Journal of Operational Research},\n number = {3}\n}

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\n \n\n \n \n \n \n \n Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis.\n \n \n \n\n\n \n Xie, C.\n\n\n \n\n\n\n Transportation Letters. .\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Estimation and Application of a Bayesian Network Model for Discrete Travel Choice Analysis},\n type = {article},\n id = {dab7115e-2b16-3b2d-8bd0-1458ce1aa9e6},\n created = {2020-01-06T20:18:13.160Z},\n accessed = {2020-01-06},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10},\n last_modified = {2020-01-06T20:18:13.160Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Xie, Chi},\n journal = {Transportation Letters}\n}

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