Bayesian Inference for Probabilistic Risk Assessment A Practitioner's Guidebook

Bayesian Inference for Probabilistic Risk Assessment A Practitioner's Guidebook / by Dana Kelly, Curtis Smith.

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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Bibliographic Details
Main Authors: Kelly, Dana (Author), Smith, Curtis (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2011.
Edition:1st ed. 2011.
Series:Springer Series in Reliability Engineering,
Springer eBook Collection.
Subjects:
Quality control.
Reliability.
Industrial safety.
Statistics .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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