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 exible 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 identication of prior-data conict at the system level based on component level test data. New results on rst-order stochastic dominance for the BetaBinomial 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.
['Gm Gero Walter', 'Louis J. M. Aslett', 'Frank P. A. Coolen']
Bayesian Nonparametric System Reliability using Sets of Priors