A modern approach to Bayesian inference for riskand reliability analysis

Recent years have seen significant advances in the use of risk analysis in a variety of applications. Because risk and reliability models are intended to support these applications, it is critical that inference methods used in these models be robust and technically sound. The inference method described in this paper is that of Bayesian Inference. The inference we describe uses a modern computational approach known as Markov chain Monte Carlo (MCMC). MCMC methods work for simple cases, but more importantly, they work efficiently on very complex cases. Recently, with the advance of computing power and improved analysis algorithms, MCMC is increasingly being used for a variety of Bayesian inference problems. In the paper, an open-source program called OpenBUGS (commonly referred to as WinBUGS) is used to solve the inference problems that are described. The approach that is taken is to provide analysis "building blocks" that can be modified, combined, or used as-is to solve a variety of challenging problems, This paper provides an overview of guidelines for the evaluation of risk and reliability-related data, It is aimed at those familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques..