Bayesian Inference for Probabilistic Risk Assessment A Practitioner's Guidebook

Bayesian Inference for Probabilistic Risk Assessment A Practitioner's Guidebook / by Dana Kelly, Curtis Smith.

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 implemen...

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Bibliographic Details
Main Authors: Kelly, Dana (Author), Smith, Curtis (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2011.
Edition:1st ed. 2011.
Series:Springer Series in Reliability Engineering,
Springer eBook Collection.
Subjects:
Quality control.
Reliability.
Industrial safety.
Statistics .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary: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.
Physical Description:XII, 228 p. online resource.
ISBN:9781849961875
ISSN:1614-7839
DOI:10.1007/978-1-84996-187-5

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