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Bayesian nonparametric system reliability using sets of priors.

Walter, G. and Aslett, L.J.M. and Coolen, F.P.A. (2017) 'Bayesian nonparametric system reliability using sets of priors.', International journal of approximate reasoning., 80 (1). pp. 67-88.

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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1016/j.ijar.2016.08.005
Publisher statement:© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date accepted:24 August 2016
Date deposited:24 August 2016
Date of first online publication:29 August 2016
Date first made open access:No date available

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