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Bayesian nonparametric models for combining heterogeneous reliability data

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  • Richard L Warr
  • David H Collins

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.

Suggested Citation

  • Richard L Warr & David H Collins, 2014. "Bayesian nonparametric models for combining heterogeneous reliability data," Journal of Risk and Reliability, , vol. 228(2), pages 166-175, April.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:2:p:166-175
    DOI: 10.1177/1748006X13503319
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    References listed on IDEAS

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    1. Blum, J. & Susarla, V., 1977. "On the posterior distribution of a dirichlet process given randomly right censored observations," Stochastic Processes and their Applications, Elsevier, vol. 5(3), pages 207-211, July.
    2. Stephen G. Walker & Paul Damien & PuruShottam W. Laud & Adrian F. M. Smith, 1999. "Bayesian Nonparametric Inference for Random Distributions and Related Functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 485-527.
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    Cited by:

    1. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.

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