Overview
- Formulates complex problems without becoming weighed down by mathematical detail
- Presents a modern perspective of Bayesian networks and Markov chain Monte Carlo (MCMC) sampling
- Written by experts
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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About this book
Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. 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.
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Keywords
Table of contents (13 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Inference for Probabilistic Risk Assessment
Book Subtitle: A Practitioner's Guidebook
Authors: Dana Kelly, Curtis Smith
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-1-84996-187-5
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London Limited 2011
Hardcover ISBN: 978-1-84996-186-8Published: 31 August 2011
Softcover ISBN: 978-1-4471-2708-6Published: 27 November 2013
eBook ISBN: 978-1-84996-187-5Published: 30 August 2011
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: XII, 228
Topics: Quality Control, Reliability, Safety and Risk, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences