Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook - Couverture souple

Kelly, Dana; Smith, Curtis

9781447127086: Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook

Couverture souple

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Editeur : Springer, 2013

Afficher les exemplaires de cette �dition comportant l?ISBN

1 D'occasion

De EUR 331,91

10 Neuf

De EUR 139,09

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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

Pr�sentation de l'�diteur

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 � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2013
Langue: anglais
ISBN 10: 1447127080
ISBN 13: 9781447127086
Reliure: Broch�
Nombre de pages: 240
Coordonn�es du fabricant: non disponible

Acheter D'occasion

�tat : Comme neuf

Like New

Afficher cet article

EUR 331,91

Autre devise

EUR 29,29 exp�dition depuis Royaume-Uni vers France

Destinations, frais et d�lais

Ajouter au panier

Acheter neuf

Afficher cet article

EUR 139,09

Autre devise

EUR 11 exp�dition depuis Allemagne vers France

Destinations, frais et d�lais

Ajouter au panier

Autres �ditions populaires du m�me titre

9781849961868: Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook

Edition pr�sent�e

ISBN 10 : 1849961867 ISBN 13 : 9781849961868
Editeur : Springer London Ltd, 2011
Couverture rigide

Springer, 2011 (Couverture souple)

R�sultats de recherche pour Bayesian Inference for Probabilistic Risk Assessment:...

Image fournie par le vendeur

Bayesian Inference for Probabilistic Risk Assessment

Curtis Smith

Edit� par Springer London Nov 2013, 2013

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. 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 r�f. du vendeur 9781447127086

Contacter le vendeur

Acheter neuf

EUR 139,09

Autre devise

Frais de port : EUR 11

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Bayesian Inference for Probabilistic Risk Assessment

Dana Kelly|Curtis Smith

Edit� par Springer London, 2013

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Neuf Kartoniert / Broschiert

impression � la demande

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Kartoniert / Broschiert. 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 r�f. du vendeur 4184642

Contacter le vendeur

Acheter neuf

EUR 206,40

Autre devise

Frais de port : EUR 9,70

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook (Springer Series in Reliability Engineering)

Kelly, Dana; Smith, Curtis

Edit� par Springer, 2013

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Neuf Couverture souple

Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Etat : New. In. N� de r�f. du vendeur ria9781447127086_new

Contacter le vendeur

Acheter neuf

EUR 230,79

Autre devise

Frais de port : EUR 4,68

De Royaume-Uni vers France

Destinations, frais et d�lais

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Bayesian Inference for Probabilistic Risk Assessment : A Practitioner's Guidebook

Curtis Smith

Edit� par Springer London, Springer London, 2013

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. 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 r�f. du vendeur 9781447127086

Contacter le vendeur

Acheter neuf

EUR 249,04

Autre devise

Frais de port : EUR 10,99

De Allemagne vers France

Destinations, frais et d�lais

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Bayesian Inference for Probabilistic Risk Assessment

Curtis Smith

Edit� par Springer London, Springer London Nov 2013, 2013

ISBN 10 : 1447127080 ISBN 13 : 9781447127086

Neuf Taschenbuch

impression � la demande

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : 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 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 r�f. du vendeur 9781447127086

Contacter le vendeur