Bayesian inference from type II doubly censored Rayleigh data

In this paper we present a Bayesian approach to inference in reliability studies based on type II doubly censored data from a Rayleigh distribution. We also consider the problem of predicting an independent future sample from the same distribution in a Bayesian setting. The results can be used to predict the failure-time of a k-out-of-m system. Bayes estimators are obtained in nice closed forms. Highest posterior density (HPD) and maximum likelihood (ML) estimators, and HPD intervals can readily be computed using iterative methods. (C) 2000 Elsevier Science B.V. All rights reserved.