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Bayesian Inference for Probabilistic Risk Assessment: A Practitioner’s Guidebook (Springer Series in Reliability Engineering)

Format Post in Control Theory BY Curtis Smith, Dana Kelly

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Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. The Author of this Book is Curtis Smith, Dana Kelly Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). Array ISBN . The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. Bayesian Inference for Probabilistic Risk Assessment: A Practitioner’s Guidebook (Springer Series in Reliability Engineering) available in English. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.

Bayesian Inference for Probabilistic Risk Assessment: A Practitioner’s Guidebook (Springer Series in Reliability Engineering)

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