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\n  \n 2020\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n A probabilistic approach for economic evaluation of occupational health and safety interventions: a case study of silica exposure reduction interventions in the construction sector.\n \n \n \n \n\n\n \n Mofidi, A.; Tompa, E.; Mortazavi, S., B.; Esfahanipour, A.; and Demers, P., A.\n\n\n \n\n\n\n BMC Public Health, 20(1): 210. 12 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"AWebsite\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 = {A probabilistic approach for economic evaluation of occupational health and safety interventions: a case study of silica exposure reduction interventions in the construction sector},\n type = {article},\n year = {2020},\n keywords = {Biostatistics,Environmental Health,Epidemiology,Medicine/Public Health,Public Health,Vaccine,general},\n pages = {210},\n volume = {20},\n websites = {https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-8307-7},\n month = {12},\n publisher = {BioMed Central},\n day = {11},\n id = {bff405b5-057f-3904-a52c-e9e584ac2ff2},\n created = {2020-02-17T21:50:30.775Z},\n accessed = {2020-02-17},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2020-02-17T21:50:36.015Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situations. We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables affect interventions net benefit. A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention. This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.},\n bibtype = {article},\n author = {Mofidi, Amirabbas and Tompa, Emile and Mortazavi, Seyed Bagher and Esfahanipour, Akbar and Demers, Paul A.},\n doi = {10.1186/s12889-020-8307-7},\n journal = {BMC Public Health},\n number = {1}\n}
\n
\n\n\n
\n Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situations. We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables affect interventions net benefit. A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention. This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.\n
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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n \n Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks.\n \n \n \n \n\n\n \n Uyar, U.; and Hatipoglu, F., B.\n\n\n \n\n\n\n Business and Economics Research Journal, 10(4): 807-822. 7 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ExaminingWebsite\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 = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},\n type = {article},\n year = {2019},\n pages = {807-822},\n volume = {10},\n websites = {http://www.berjournal.com/?p=4515},\n month = {7},\n day = {29},\n id = {28a18189-8724-3be7-9aa9-319a65152315},\n created = {2019-07-29T18:11:51.717Z},\n accessed = {2019-07-29},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2019-12-29T16:16:28.732Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Uyar, Umut and Hatipoglu, Fatma Busem},\n doi = {10.20409/berj.2019.202},\n journal = {Business and Economics Research Journal},\n number = {4}\n}
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\n  \n 2018\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Causal data science for financial stress testing.\n \n \n \n \n\n\n \n Gao, G.; Mishra, B.; and Ramazzotti, D.\n\n\n \n\n\n\n Journal of Computational Science. 4 2018.\n \n\n\n\n
\n\n\n\n \n \n \"CausalWebsite\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 = {Causal data science for financial stress testing},\n type = {article},\n year = {2018},\n websites = {https://www.sciencedirect.com/science/article/pii/S1877750317311377#!},\n month = {4},\n publisher = {Elsevier},\n day = {7},\n id = {ea206663-14f7-397a-9d8d-666e281286c5},\n created = {2018-04-24T13:26:07.749Z},\n accessed = {2018-04-19},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2018-04-24T13:26:07.749Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs); SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo simulations.},\n bibtype = {article},\n author = {Gao, Gelin and Mishra, Bud and Ramazzotti, Daniele},\n doi = {10.1016/J.JOCS.2018.04.003},\n journal = {Journal of Computational Science}\n}
\n
\n\n\n
\n The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs); SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo simulations.\n
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\n \n\n \n \n \n \n \n \n Financial Centres’ Polyarchy and Competitiveness Does Political Participation Change a Financial Centre’s Competitiveness?.\n \n \n \n \n\n\n \n Michael, B.; and Candelon, B., [., 1.\n\n\n \n\n\n\n . 2018.\n \n\n\n\n
\n\n\n\n \n \n \"FinancialWebsite\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\n
\n 
@article{\n title = {Financial Centres’ Polyarchy and Competitiveness Does Political Participation Change a Financial Centre’s Competitiveness?},\n type = {article},\n year = {2018},\n keywords = {330,Bayesian network analysis,D72,F33,F55,P48,dynamic polyarchy,endogenous global city network centrality,international financial centres},\n websites = {https://www.econstor.eu/handle/10419/177221},\n publisher = {Kiel und Hamburg: ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft},\n id = {4b22b9d5-09e2-3786-9ac9-4fe26cc566b9},\n created = {2018-04-25T01:34:14.363Z},\n accessed = {2018-04-24},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2018-04-25T01:34:14.363Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Michael, Bryane and Candelon, Bertrand [PND:] 171495934}\n}
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\n
\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree.\n \n \n \n \n\n\n \n Chen, F.; Chi, D.; and Wang, Y.\n\n\n \n\n\n\n Economic Modelling, 46: 1-10. 4 2015.\n \n\n\n\n
\n\n\n\n \n \n \"DetectingWebsite\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 = {Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree},\n type = {article},\n year = {2015},\n keywords = {Accrual earnings management,Back propagation neural network (BPN),Bayesian network (BN),C5.0 decision tree,C8,Data mining,G3,M1,M4,Principal component analysis (PCA)},\n pages = {1-10},\n volume = {46},\n websites = {http://www.sciencedirect.com/science/article/pii/S0264999314005094},\n month = {4},\n id = {70697504-f3b4-3db4-be14-bdc4c3934817},\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 = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The characteristic of long value chain, high-risk, high cost of research and development are belong to high knowledge based content in the biotech medical industry, and the reliability of biotechnology industry's financial statements and the earnings management behavior conducted by the management in their accrual manipulation have been a critical issue. In recent years, some studies have used the data mining technique to detect earnings management, with which the accuracy has therefore risen. As such, this study attempts to diagnose the detecting biotechnology industry earnings management by integrating suitable computing models, we first screened the earnings management variables with the principal component analysis (PCA) and Bayesian network (BN), followed by further constructing the integrated model with the back propagation neural network (BPN) and C5.0 (decision tree) to detect if a company's earnings were seriously manipulated. The empirical results show that combining the BN screening method with C5.0 decision tree has the best performance with an accuracy rate of 98.51%. From the rules set in the final additional testing of the study, it is also found that an enterprise's prior period discretionary accruals play an important role in affecting the serious degree of accrual earnings management.},\n bibtype = {article},\n author = {Chen, Fu-Hsiang and Chi, Der-Jang and Wang, Yi-Cheng},\n doi = {10.1016/j.econmod.2014.12.035},\n journal = {Economic Modelling}\n}
\n
\n\n\n
\n The characteristic of long value chain, high-risk, high cost of research and development are belong to high knowledge based content in the biotech medical industry, and the reliability of biotechnology industry's financial statements and the earnings management behavior conducted by the management in their accrual manipulation have been a critical issue. In recent years, some studies have used the data mining technique to detect earnings management, with which the accuracy has therefore risen. As such, this study attempts to diagnose the detecting biotechnology industry earnings management by integrating suitable computing models, we first screened the earnings management variables with the principal component analysis (PCA) and Bayesian network (BN), followed by further constructing the integrated model with the back propagation neural network (BPN) and C5.0 (decision tree) to detect if a company's earnings were seriously manipulated. The empirical results show that combining the BN screening method with C5.0 decision tree has the best performance with an accuracy rate of 98.51%. From the rules set in the final additional testing of the study, it is also found that an enterprise's prior period discretionary accruals play an important role in affecting the serious degree of accrual earnings management.\n
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\n
\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n ON THE STRUCTURE OF FINANCIAL CONTAGION: ECONOMETRIC TESTS AND MERCOSUR EVIDENCE.\n \n \n \n \n\n\n \n Viale, A., M.; Bessler, D., A.; and Kolari, J., W.\n\n\n \n\n\n\n Journal of Applied Economics, 17(2): 373-400. 11 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ONWebsite\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 
@article{\n title = {ON THE STRUCTURE OF FINANCIAL CONTAGION: ECONOMETRIC TESTS AND MERCOSUR EVIDENCE},\n type = {article},\n year = {2014},\n keywords = {Bayesian belief networks,C14,C32,C51,G15,copulae,directed acyclic graphs,financial contagion},\n pages = {373-400},\n volume = {17},\n websites = {http://www.sciencedirect.com/science/article/pii/S1514032614600179},\n month = {11},\n id = {ba567135-90e5-3d9d-8687-40f8dc64a7a0},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-03-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We introduce a flexible copula-based semi-parametric test of financial contagion that is capable of capturing structural shifts in the transmission channel of shocks across a network of financial markets beyond the increase in the intensity of time-varying dependence. We illustrate the capabilities of the proposed test using returns of stock, money, sovereign debt, and foreign exchange markets of seven Latin-American countries, and test for the presence of pure contagion effects for each major financial crisis that affected the Mercosur region between 1994 and 2001. Besides strong evidence in favor of time-varying market interdependence, we cannot rule out the presence of pure contagion effects in the stock market transmission channel associated with the Mexican, Asian, and Russian financial crises.},\n bibtype = {article},\n author = {Viale, Ariel M. and Bessler, David A. and Kolari, James W.},\n doi = {10.1016/S1514-0326(14)60017-9},\n journal = {Journal of Applied Economics},\n number = {2}\n}
\n
\n\n\n
\n We introduce a flexible copula-based semi-parametric test of financial contagion that is capable of capturing structural shifts in the transmission channel of shocks across a network of financial markets beyond the increase in the intensity of time-varying dependence. We illustrate the capabilities of the proposed test using returns of stock, money, sovereign debt, and foreign exchange markets of seven Latin-American countries, and test for the presence of pure contagion effects for each major financial crisis that affected the Mercosur region between 1994 and 2001. Besides strong evidence in favor of time-varying market interdependence, we cannot rule out the presence of pure contagion effects in the stock market transmission channel associated with the Mexican, Asian, and Russian financial crises.\n
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\n  \n 2013\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index.\n \n \n \n \n\n\n \n Sohn, S., Y.; and Lee, A., S.\n\n\n \n\n\n\n Expert Systems with Applications, 40(10): 4003-4009. 8 2013.\n \n\n\n\n
\n\n\n\n \n \n \"BayesianWebsite\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 = {Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index},\n type = {article},\n year = {2013},\n keywords = {Bayesian network,Early stage entrepreneurial activity Index,Forecasting,GEM},\n pages = {4003-4009},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S0957417413000122},\n month = {8},\n id = {36183c2e-d033-306b-add9-5fd77765645d},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-02-14},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.},\n bibtype = {article},\n author = {Sohn, So Young and Lee, Ann Sung},\n doi = {10.1016/j.eswa.2013.01.009},\n journal = {Expert Systems with Applications},\n number = {10}\n}
\n
\n\n\n
\n Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.\n
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\n \n\n \n \n \n \n \n \n Policy making for broadband adoption and usage in Chile through machine learning.\n \n \n \n \n\n\n \n Ruz, G., A.; Varas, S.; and Villena, M.\n\n\n \n\n\n\n Expert Systems with Applications, 40(17): 6728-6734. 12 2013.\n \n\n\n\n
\n\n\n\n \n \n \"PolicyWebsite\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 = {Policy making for broadband adoption and usage in Chile through machine learning},\n type = {article},\n year = {2013},\n keywords = {Bayesian networks,Broadband penetration,Clustering analysis,Policy making},\n pages = {6728-6734},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S095741741300434X},\n month = {12},\n id = {11ee00ca-d62d-339c-be01-ba5dabeeb109},\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 = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {For developing countries, such as Chile, we study the influential factors for adoption and usage of broadband services. In particular, subsidies on the broadband price are analyzed to see if this initiative has a significant effect in the broadband penetration. To carry out this study, machine learning techniques are used to identify different household profiles using the data obtained from a survey on access, use, and users of broadband Internet from Chile. Different policies are proposed for each group found, which were then evaluated empirically through Bayesian networks. Results show that an unconditional subsidy for the Internet price does not seem to be very appropriate for everyone since it is only significant for some households groups. The evaluation using Bayesian networks showed that other polices should be considered as well such as the incorporation of computers, Internet applications development, and digital literacy training.},\n bibtype = {article},\n author = {Ruz, Gonzalo A. and Varas, Samuel and Villena, Marcelo},\n doi = {10.1016/j.eswa.2013.06.039},\n journal = {Expert Systems with Applications},\n number = {17}\n}
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\n For developing countries, such as Chile, we study the influential factors for adoption and usage of broadband services. In particular, subsidies on the broadband price are analyzed to see if this initiative has a significant effect in the broadband penetration. To carry out this study, machine learning techniques are used to identify different household profiles using the data obtained from a survey on access, use, and users of broadband Internet from Chile. Different policies are proposed for each group found, which were then evaluated empirically through Bayesian networks. Results show that an unconditional subsidy for the Internet price does not seem to be very appropriate for everyone since it is only significant for some households groups. The evaluation using Bayesian networks showed that other polices should be considered as well such as the incorporation of computers, Internet applications development, and digital literacy training.\n
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\n \n\n \n \n \n \n \n \n General Bayesian Network Approach to Health Informatics Prediction: Emphasis on Performance Comparison.\n \n \n \n \n\n\n \n Chung, D.; Lee, K., C.; and Seong, S., C.\n\n\n \n\n\n\n Procedia - Social and Behavioral Sciences, 81: 465-468. 6 2013.\n \n\n\n\n
\n\n\n\n \n \n \"GeneralWebsite\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 = {General Bayesian Network Approach to Health Informatics Prediction: Emphasis on Performance Comparison},\n type = {article},\n year = {2013},\n keywords = {General Bayesian Network,Health Informatics,Naïve Bayesian Network,Tree-Augmented NBN},\n pages = {465-468},\n volume = {81},\n websites = {http://www.sciencedirect.com/science/article/pii/S1877042813015280},\n month = {6},\n id = {a2b519f7-fc4e-362c-82b0-6ae0a9733ddb},\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 = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Health Informatics is emerging as a promising research area. As average life expectancy increases due to medical technology development, health issues remain most sensitive agenda in most of countries in the world. However, heal th technology requires more intelligent mechanisms by which users’ requirement for more accurate prediction about their health problems can be fulfilled. Furthermore, such intelligent mechanisms must provide very flexible and robust procedures by which complicated but necessary decision support functions are allowed. In this sense, this paper proposes General Bayesian Network (GBN) to predict appropriate diets and restaurants that would benefit users’ health. We compared the performance of GBN with other competing techniques such as NBN (naive Bayesian Network), TAN (Tree Augmented naive Bayesian Network), and decision tree. Experiments with real health dataset revealed that GBN results outperform other techniques with statistical validity.},\n bibtype = {article},\n author = {Chung, Dahee and Lee, Kun Chang and Seong, Seung Chang},\n doi = {10.1016/j.sbspro.2013.06.461},\n journal = {Procedia - Social and Behavioral Sciences}\n}
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\n Health Informatics is emerging as a promising research area. As average life expectancy increases due to medical technology development, health issues remain most sensitive agenda in most of countries in the world. However, heal th technology requires more intelligent mechanisms by which users’ requirement for more accurate prediction about their health problems can be fulfilled. Furthermore, such intelligent mechanisms must provide very flexible and robust procedures by which complicated but necessary decision support functions are allowed. In this sense, this paper proposes General Bayesian Network (GBN) to predict appropriate diets and restaurants that would benefit users’ health. We compared the performance of GBN with other competing techniques such as NBN (naive Bayesian Network), TAN (Tree Augmented naive Bayesian Network), and decision tree. Experiments with real health dataset revealed that GBN results outperform other techniques with statistical validity.\n
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\n  \n 2007\n \n \n (1)\n \n \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 \"BayesianWebsite\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 = {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 = {da92f185-8927-3ef5-91ff-52b009efad6e},\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 = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\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}
\n
\n\n\n
\n 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
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\n  \n 2004\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Statistical models for operational risk management.\n \n \n \n \n\n\n \n Cornalba, C.; and Giudici, P.\n\n\n \n\n\n\n Physica A: Statistical Mechanics and its Applications, 338(1-2): 166-172. 7 2004.\n \n\n\n\n
\n\n\n\n \n \n \"StatisticalWebsite\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 = {Statistical models for operational risk management},\n type = {article},\n year = {2004},\n keywords = {02.50.−r,89.65.Gh,Bayesian networks,Operational risk management,Predictive models,Value at risk},\n pages = {166-172},\n volume = {338},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378437104002341},\n month = {7},\n id = {1d9d6c56-9d4d-3ac2-9541-6791eda3c24d},\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 = {9e675e19-5395-384f-b059-398ce8d4de97},\n last_modified = {2017-03-14T14:28:58.104Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.},\n bibtype = {article},\n author = {Cornalba, Chiara and Giudici, Paolo},\n doi = {10.1016/j.physa.2004.02.039},\n journal = {Physica A: Statistical Mechanics and its Applications},\n number = {1-2}\n}
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\n The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.\n
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\n"}; document.write(bibbase_data.data);