ABSTRACT
This chapter introduces the Bayesian approach to the analysis of reliability data. Up to this point, statistical inference has been discussed from the classical, or frequentist, point of view. That is, estimators and test statistics are assessed by criteria relating to their performance in repeated sampling. In the Bayesian approach, direct probability statements are made about unknown quantities, conditional on the observed data. From the point of view of reliability data analysis, the adoption of a Bayesian approach has a number of practical advantages. In reliability analysis, a specific area where it is essential to work with realistic prior distributions is in the assessment of component reliability using component and/or systems data. It is clear that the derivation of posterior quantities of interest, such as highest posterior density (HPD) regions, posterior moments, marginal posterior distributions and predictive distributions, requires integration techniques. In many reliability problems, the purpose of collecting the reliability data is to help in solving a decision problem.
