Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook - Couverture rigide
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook (Springer Series in Reliability Engineering)
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Springer, 2011
ISBN 10 : 1849961867
ISBN 13 : 9781849961868
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Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook (Springer Series in Reliability Engineering)
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Springer, 2011
ISBN 10 : 1849961867
ISBN 13 : 9781849961868
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Etat : 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 Probabilis. N° de réf. du vendeur 4288574
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Bayesian Inference for Probabilistic Risk Assessment
Edité par
Springer London, Springer London Aug 2011, 2011
ISBN 10 : 1849961867
ISBN 13 : 9781849961868
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Buch. Etat : 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 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. 240 pp. Englisch. N° de réf. du vendeur 9781849961868
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Bayesian Inference for Probabilistic Risk Assessment | A Practitioner's Guidebook
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Buch. Etat : Neu. Bayesian Inference for Probabilistic Risk Assessment | A Practitioner's Guidebook | Curtis Smith (u. a.) | Buch | xii | Englisch | 2011 | Springer | EAN 9781849961868 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 101290752
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Bayesian Inference for Probabilistic Risk Assessment
Edité par
Springer London, Springer London Aug 2011, 2011
ISBN 10 : 1849961867
ISBN 13 : 9781849961868
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. 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 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch. N° de réf. du vendeur 9781849961868
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Bayesian Inference for Probabilistic Risk Assessment : A Practitioner's Guidebook
Edité par
Springer London, Springer, 2011
ISBN 10 : 1849961867
ISBN 13 : 9781849961868
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : 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 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. N° de réf. du vendeur 9781849961868
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