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...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| 格式: | Livre numérique |
| 語言: | Anglais |
| 出版: |
London :
Springer London
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2011. |
| 叢編: | Springer Series in Reliability Engineering
|
| 在線閱讀: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 提示: |
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 |
書本目錄:
- 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