Bayesian inference in probabilistic risk assessment—The current state of the art

Dana L. Kelly and Curtis L. Smith

Reliability Engineering and System Safety, 2009, vol. 94, issue 2, 628-643

Abstract: Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via MCMC sampling to a variety of important problems.

Keywords: Bayesian inference; Markov chain Monte Carlo; Parameter estimation; Model validation; Probabilistic risk analysis (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:94:y:2009:i:2:p:628-643

DOI: 10.1016/j.ress.2008.07.002

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