Bayesian parameter estimation of strength distribution for highly accelerated life testing data

Abstract

HALT(Highly Accelerated Life Test) technique is an accelerated method which uses stresses higher than the field environments to expose and then improve design weakness which can be explained stress-strength model. Because HALT is conducted at the design phase, there are some constraints to analyze the data. For analyzing HALT data through step-stress tests, It is very important to estimate parametersof strength distribution accurately. In estimating parameters of strength distribution, ML (maximum likelihood) methods have been widely used. We propose a Bayesian method to model the HALT data for both complete data and censored data. Metropolis-Hasting algorithm is used to estimate posterior distribution.