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dc.contributor.authorJoshi, Chaitanyaen_NZ
dc.contributor.authorRuggeri, Fabrizioen_NZ
dc.contributor.authorWilson, Simon P.en_NZ
dc.date.accessioned2019-06-05T00:03:09Z
dc.date.available2018-03-01en_NZ
dc.date.available2019-06-05T00:03:09Z
dc.date.issued2018en_NZ
dc.identifier.citationJoshi, C., Ruggeri, F., & Wilson, S. P. (2018). Prior Robustness for Bayesian Implementation of the Fault Tree Analysis. IEEE Transactions on Reliability, 67(1), 170–183. https://doi.org/10.1109/TR.2017.2778241en
dc.identifier.issn0018-9529en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12595
dc.description.abstractWe propose a prior robustness approach for the Bayesian implementation of the fault tree analysis (FTA). FTA is often used to evaluate risk in large, safety critical systems but has limitations due to its static structure. Bayesian approaches have been proposed as a superior alternative to it, however, this involves prior elicitation, which is not straightforward. We show that minor misspecification of priors for elementary events can result in a significant prior misspecification for the top event. A large amount of data is required to correctly update a misspecified prior and such data may not be available for many complex, safety critical systems. In such cases, prior misspecification equals posterior misspecification. Therefore, there is a need to develop a robustness approach for FTA, which can quantify the effects of prior misspecification on the posterior analysis. Here, we propose the first prior robustness approach specifically developed for FTA. We not only prove a few important mathematical properties of this approach, but also develop easy to use Monte Carlo sampling algorithms to implement this approach on any given fault tree with and and/or or gates. We then implement this Bayesian robustness approach on two real-life examples: a spacecraft re-entry example and a feeding control system example. We also provide a step-by-step illustration of how this approach can be applied to a real-life problem.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherIEEEen_NZ
dc.rightsThis is the author's accepted version. © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Hardware & Architectureen_NZ
dc.subjectComputer Science, Software Engineeringen_NZ
dc.subjectEngineering, Electrical & Electronicen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectEngineeringen_NZ
dc.subjectBayesian networks (BNs)en_NZ
dc.subjectBayesian robustnessen_NZ
dc.subjectdistorted band of priorsen_NZ
dc.subjectfault tree analysis (FTA)en_NZ
dc.subjectprior elicitationen_NZ
dc.titlePrior Robustness for Bayesian Implementation of the Fault Tree Analysisen_NZ
dc.typeJournal Article
dc.identifier.doi10.1109/TR.2017.2778241en_NZ
dc.relation.isPartOfIEEE Transactions on Reliabilityen_NZ
pubs.begin-page170
pubs.elements-id204356
pubs.end-page183
pubs.issue1en_NZ
pubs.publication-statusPublisheden_NZ
pubs.volume67en_NZ
dc.identifier.eissn1558-1721en_NZ


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