Springer series in reliability engineering - 9781849961868 - bayesian inference for probabilistic risk assessment: a practitioner's guidebook di kelly, dana; smith, curtis (9 risultati)
Lingua: Inglese
Editore: Springer, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Lingua: Inglese
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Paperback. Condizione: Good. Good - . In good condition.
Lingua: Inglese
Editore: Springer, 2011
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Condizione: New. In.
Lingua: Inglese
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 C…arlo (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.
Lingua: Inglese
Editore: Springer Verlag, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Hardcover. Condizione: Brand New. 237 pages. 9.25x6.25x0.50 inches. In Stock.
Lingua: Inglese
Editore: Springer, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer London, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Gebunden. Condizione: New. 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 Infe…rence for Probabilis.
Lingua: Inglese
Editore: Springer London, Springer London Aug 2011, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Mark…ov 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. 240 pp. Englisch.
Lingua: Inglese
Editore: Springer London, Springer Aug 2011, 2011
Serie: Libro 24 di 90 - Springer Series in Reliability Engineering
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 c…hain 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch.
