Bayesian Inference for Probabilistic Risk Assessment

Kelly, Dana; Smith, Curtis

ISBN 10: 1849961867 / ISBN 13: 9781849961868

Editore: Springer London Ltd, 2011

This book synthesizes significant recent advances in the use of risk analysis in many government agencies and private corporations, providing a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Series: Springer Series in Reliability Engineering. Num Pages: 228 pages, 117 black & white tables, biography. BIC Classification: PDE; TBJ; TGPQ; TGPR. Category: (P) Professional & Vocational. Dimension: 241 x 162 x 19. Weight in Grams: 498. . 2011. Hardback. . . . . Books ship from the US and Ireland. Codice articolo V9781849961868

Riassunto:

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.

Dalla quarta di copertina:

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.

Dati bibliografici

Titolo: Bayesian Inference for Probabilistic Risk ...
Casa editrice: Springer London Ltd
Data di pubblicazione: 2011
Legatura: Rilegato
Condizione: New