Bayesian Inference for Probabilistic Risk Assessment

A Practitioner's Guidebook

  • Dana Kelly
  • Curtis Smith
Book

Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Dana Kelly, Curtis Smith
    Pages 1-6
  3. Dana Kelly, Curtis Smith
    Pages 7-13
  4. Dana Kelly, Curtis Smith
    Pages 15-38
  5. Dana Kelly, Curtis Smith
    Pages 39-50
  6. Dana Kelly, Curtis Smith
    Pages 51-60
  7. Dana Kelly, Curtis Smith
    Pages 61-65
  8. Dana Kelly, Curtis Smith
    Pages 67-88
  9. Dana Kelly, Curtis Smith
    Pages 89-109
  10. Dana Kelly, Curtis Smith
    Pages 111-122
  11. Dana Kelly, Curtis Smith
    Pages 123-140
  12. Dana Kelly, Curtis Smith
    Pages 141-163
  13. Dana Kelly, Curtis Smith
    Pages 165-176
  14. Dana Kelly, Curtis Smith
    Pages 177-199
  15. Back Matter
    Pages 201-225

About this book

Introduction

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.

Keywords

Bayesian Inference Bayesian Networks CP6917 MCMC Probabilistic Risk Assessment Reliability

Authors and affiliations

  • Dana Kelly
    • 1
  • Curtis Smith
    • 2
  1. 1.Environmental Laboratory (INEEL), Subsurface Science InitiativeIdaho National Engineering &Idaho FallsUSA
  2. 2.Environmental Laboratory (INEEL), Subsurface Science InitiativeIdaho National Engineering &Idaho FallsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84996-187-5
  • Copyright Information Springer-Verlag London Limited 2011
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-84996-186-8
  • Online ISBN 978-1-84996-187-5
  • Series Print ISSN 1614-7839
  • About this book