Bayesian Inference for Probabilistic Risk Assessment

Dana Kelly|Curtis Smith

ISBN 10: 1849961867 / ISBN 13: 9781849961868

Verlag: Springer London, 2011

Beschreibung

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Formulates complex problems without becoming weighed down by mathematical detailPresents a modern perspective of Bayesian networks and Markov chain Monte Carlo (MCMC) samplingWritten by expertsBayesian Inference for Probabilis.

Bestandsnummer des Verkäufers 4288574

Bibliografische Details

• Titel: Bayesian Inference for Probabilistic Risk Assessment
• Verlag: Springer London
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 1849961867
• ISBN-13: 9781849961868
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Dana Kelly and Curtis Smith are both specialists in Bayesian inference for risk and reliability analysis, working at the Idaho National Laboratory, USA. They provide support to the Nuclear Regulatory Commission, NASA, the Joint Research Centre in Pettern, and others. They are the authors of numerous refereed publications in the field.