@article{
 title = {Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas},
 type = {article},
 year = {2012},
 keywords = {Bayesian networks,Dependability,Maintenance,Reliability,Risk analysis,Safety},
 pages = {671-682},
 volume = {25},
 websites = {http://www.sciencedirect.com/science/article/pii/S095219761000117X},
 month = {6},
 id = {c035410e-7315-318d-8566-ccdf6c07c089},
 created = {2015-04-11T19:52:04.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {4a6a1914-6ba6-3cdc-b1f4-f10a6e56cb6c},
 last_modified = {2017-03-14T14:27:43.598Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, a bibliographical review over the last decade is presented on the application of Bayesian networks to dependability, risk analysis and maintenance. It is shown an increasing trend of the literature related to these domains. This trend is due to the benefits that Bayesian networks provide in contrast with other classical methods of dependability analysis such as Markov Chains, Fault Trees and Petri Nets. Some of these benefits are the capability to model complex systems, to make predictions as well as diagnostics, to compute exactly the occurrence probability of an event, to update the calculations according to evidences, to represent multi-modal variables and to help modeling user-friendly by a graphical and compact approach. This review is based on an extraction of 200 specific references in dependability, risk analysis and maintenance applications among a database with 7000 Bayesian network references. The most representatives are presented, then discussed and some perspectives of work are provided.},
 bibtype = {article},
 author = {Weber, P. and Medina-Oliva, G. and Simon, C. and Iung, B.},
 doi = {10.1016/j.engappai.2010.06.002},
 journal = {Engineering Applications of Artificial Intelligence},
 number = {4}
}

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