Bayesian nonparametric models for combining heterogeneous reliability data

Richard L Warr and David H Collins

Journal of Risk and Reliability, 2014, vol. 228, issue 2, 166-175

Abstract: Modern complex engineering systems often present the analyst with a mix of data types that can be used for reliability prediction: system test results, lifetime data from unit tests of components, and subsystem data, all of which may have predictive value for the system lifetime. We present a hierarchical nonparametric framework, using Dirichlet processes, in which time-to-event distributions may be estimated from sample data or derived based on physical failure mechanisms. By applying a Bayesian methodology, the framework can incorporate prior information, including expert opinion.

Keywords: Dirichlet process; hierarchical modeling; lifetime prediction; parallel and series systems (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:228:y:2014:i:2:p:166-175

DOI: 10.1177/1748006X13503319

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