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Titlebook: Bayesian Inference for Probabilistic Risk Assessment; A Practitioner‘s Gui Dana Kelly,Curtis Smith Book 2011 Springer-Verlag London Limited

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期刊全称Bayesian Inference for Probabilistic Risk Assessment
期刊简称A Practitioner‘s Gui
影响因子2023Dana Kelly,Curtis Smith
视频video
发行地址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
学科分类Springer Series in Reliability Engineering
图书封面Titlebook: Bayesian Inference for Probabilistic Risk Assessment; A Practitioner‘s Gui Dana Kelly,Curtis Smith Book 2011 Springer-Verlag London Limited
影响因子.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
Pindex Book 2011
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Modeling Failure with Repair,s not repaired, and the component or system is replaced following failure, then the earlier analysis methods are applicable. However, in this chapter, we consider the case in which the failed component or system is repaired and placed back into service.
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Bayesian Treatment of Uncertain Data,pect to observed data. For example, the number of binomial demands may not have been recorded and may have to be estimated. Similarly, the exposure time in the Poisson distribution may have to be estimated. One may not always be able to tell the exact number of failures that have occurred, because o
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Bayesian Inference for Multilevel Fault Tree Models,ysis framework to perform probabilistic inference on the model. For example, we might have information on the overall system performance, but we might also have subsystem and component level information. We demonstrate the analysis approach using a simple fault tree model containing a single top eve
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