A general Bayes exponential inference model for accelerated life testing

9 This article develops a general Bayes inference model for accelerated life testing assuming failure times at each stress level are exponentially distributed. Using the approach, Bayes 11 point estimates as well as probability statements for use-stress life parameters may be inferred from the following testing scenarios: regular life testing, 7xed-stress testing, step-stress testing, 13 pro7le-stress testing, and also mixtures thereof. The inference procedure uses the well known Markov chain Monte Carlo (MCMC) methods to derive posterior quantities and accommodates 15 both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The approach is illustrated with an example. 17 c © 2002 Published by Elsevier Science B.V.