Bayesian Post-Processing for Subset Simulation for Decision Making under Risk

Abstract

Estimation of the failure probability, that is, the probability of unacceptable system performance, is an important and computationally challenging problem in reliability engineering. In cases of practical interest, the failure probability is given by an integral over a high-dimensional uncertain parameter space. Over the past decade, the engineering research community has realized the importance of advanced stochastic simulation methods for solving reliability problems. Subset Simulation, proposed by Au and Beck, provides an efficient algorithm for computing failure probabilities for general high-dimensional reliability problems. Here, a Bayesian post-processor for the original Subset Simulation method is presented that produces the posterior PDF of the failure probability which can be used in risk analyses for life-cycle cost analysis, decision making under risk, etc.

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