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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.

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Citation Information

Please cite this paper as:

Walter, G., Aslett, L. J. M. and Coolen, F. P. A. (2017), ‘Bayesian Nonparametric System Reliability using Sets of Priors’, International Journal of Approximate Reasoning 80, 67-88.

BibTeX:

@article{Walter2017,
  author={Walter, G. and Aslett, L. J. M. and Coolen, F. P. A.},
  year={2017},
  title={Bayesian Nonparametric System Reliability using Sets of Priors},
  volume={80},
  pages={67--88},
  doi={10.1016/j.ijar.2016.08.005},
  url={http://dx.doi.org/10.1016/j.ijar.2016.08.005},
  journal={International Journal of Approximate Reasoning}
}

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