Bayesian Computations for Random Environment Models

D. K. Al-Mutairi

Journal of Applied Statistics, 2004, vol. 31, issue 6, 645-659

Abstract: 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.

Keywords: Bayesian Computation; Bayesian Inference; Gibbs Sampling; Joint Prior Distribution; Random Environment (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1080/1478881042000214631

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