Bayesian Computations for Random Environment Models

This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we pre...

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Bibliographic Details
Published in:Journal of applied statistics 2004-07, Vol.31 (6), p.645-659
Main Author: Al-Mutairi, D. K.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian Computation
Bayesian Inference
Gibbs Sampling
Joint Prior Distribution
Markov analysis
Random Environment
Online Access:Get full text
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Summary:This paper deals with the analysis of reliability data from a Bayesian perspective for Random Environment (RE) models. We give an overview of current literature on RE models. We also study the computational problems associated with the implementations of RE models in a Bayesian setting. Then, we present the Markov Chain Monte Carlo technique to solve such problems. These problems arise in posterior and predictive analysis and their relevant quantities such as mean, variance, and median. The suggested methodology is incorporated with an illustration.
ISSN:0266-4763
1360-0532
DOI:10.1080/1478881042000214631