9781849961868 - bayesian inference for probabilistic risk assessment: a practitioner’s guidebook (springer series in reliability engineering) de smith, curtis; kelly, dana (8 resultados)
Idioma: Inglés
Editorial: Springer, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Idioma: Inglés
Editorial: Springer London, Springer, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Buch. Condición: 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 Ca…rlo (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.
Idioma: Inglés
Editorial: Springer Verlag, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 237 pages. 9.25x6.25x0.50 inches. In Stock.
Idioma: Inglés
Editorial: Springer, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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Hardcover. Condición: Like New. Like New. book.
Idioma: Inglés
Editorial: Springer, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer London, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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 Inference for P…robabilis.
Idioma: Inglés
Editorial: Springer London, Springer London Aug 2011, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: 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 Marko…v 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.
Idioma: Inglés
Editorial: Springer London, Springer Aug 2011, 2011
Serie: Springer Series in Reliability Engineering, Libro 24 de 90. Libro 24 de 90 - Springer Series in Reliability Engineering
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 246,09
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Buch. Condición: 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 ch…ain 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.
