Bayesian assessment of the variability of reliability measures

Abstract

Population variability analysis, also known as the first stage in two-stage Bayesian updating, is an estimation procedure for the assessment of the variability of reliability measures among a group of sub-populations of similar systems. The estimated variability distributions are used as prior distributions in system-specific Bayesian updates. In this paper we present a Bayesian approach for population variability analysis involving the use of non-conjugate variability models that works over a continuous, rather than the discretized, variability model parameter space. The cases to be discussed are the ones typically encountered by the reliability practitioner: run-time data for failure rate assessment, demand-based data for failure probability assessment, and expert-based evidence for failure rate and failure probability analysis. We outline the estimation procedure itself as well as its link with conventional Bayesian updating procedures, describe the results generated by the procedures and their behavior under various data conditions, and provide numerical examples.