Bayesian nonparametric system reliability using sets of priors

Walter, G.; Aslett, L.J.M.; Coolen, F.P.A.

Abstract

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test data these bounds are propagated to bounds on the posterior predictive distribution for the functioning probability of a new system containing components exchangeable with those used in testing. The method further enables identification of prior–data conflict at the system level based on component level test data. New results on first-order stochastic dominance for the Beta-Binomial distribution make the technique computationally tractable. Our methodological contributions can be immediately used in applications by reliability practitioners as we provide easy to use software tools.

Citation

Walter, G., Aslett, L., & Coolen, F. (2017). Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 80(1), 67-88. https://doi.org/10.1016/j.ijar.2016.08.005

• Journal Article Type: Article
• Acceptance Date: Aug 24, 2016
• Online Publication Date: Aug 29, 2016
• Publication Date: Jan 1, 2017
• Deposit Date: Aug 24, 2016
• Publicly Available Date: Aug 24, 2016
• Journal: International Journal of Approximate Reasoning
• Print ISSN: 0888-613X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Issue: 1
• Pages: 67-88
• DOI: https://doi.org/10.1016/j.ijar.2016.08.005
• Public URL: https://durham-repository.worktribe.com/output/1375955