Bayesian inference for probabilistic risk assessment : a practitioner's guidebook
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 implemen...
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| Hoofdauteurs: | , |
|---|---|
| Formaat: | Livre numérique |
| Taal: | Anglais |
| Gepubliceerd in: |
London :
Springer London
[20..].
Cham : Springer Nature |
| Editie: | 1st ed. 2011. |
| Reeks: | Springer Series in Reliability Engineering
|
| Online toegang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Opmerking: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Bayesian Inference for Probabilistic Risk Assessment, Texte imprimé, 9781849961868 • Bayesian Inference for Probabilistic Risk Assessment, Texte imprimé, 9781849961868 • Bayesian Inference for Probabilistic Risk Assessment, Texte imprimé, 9781447127086 • Bayesian Inference for Probabilistic Risk Assessment, Texte imprimé, 9781849961882 |
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| 100 | 1 | |a Kelly, Dana, |d 19..- | |
| 245 | 1 | 0 | |a Bayesian inference for probabilistic risk assessment : |b a practitioner's guidebook |c by Dana Kelly, Curtis Smith. |
| 250 | |a 1st ed. 2011. | ||
| 260 | |a London : |b Springer London. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Series in Reliability Engineering |x 2196-999X | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 1. Introduction and Motivation 2. Introduction to Bayesian Inference 3. Bayesian Inference for Common Aleatory Models 4. Bayesian Model Checking 5. Time Trends for Binomial and Poisson Data 6. Checking Convergence to Posterior Distribution 7. Hierarchical Bayes Models for Variability 8. More Complex Models for Random Durations 9. Modeling Failure with Repair 10. Bayesian Treatment of Uncertain Data 11. Bayesian Regression Models 12. Bayesian Inference for Multilevel Fault Tree Models 13. Additional Topics | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a Smith, Curtis, |d 19..- |4 aut | |
| 776 | 0 | |t Bayesian Inference for Probabilistic Risk Assessment |b Texte imprimé |z 9781849961868 | |
| 776 | 0 | |t Bayesian Inference for Probabilistic Risk Assessment |b Texte imprimé |z 9781849961868 | |
| 776 | 0 | |t Bayesian Inference for Probabilistic Risk Assessment |b Texte imprimé |z 9781447127086 | |
| 776 | 0 | |t Bayesian Inference for Probabilistic Risk Assessment |b Texte imprimé |z 9781849961882 | |
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