Bayes factors for accelerated life testing models

TL;DR

This paper explores the use of Bayes factors for model comparison in Bayesian accelerated life testing, addressing computational challenges with complex dual-stress models.

Contribution

It introduces methods for approximating marginal likelihoods to facilitate Bayesian model comparison in complex accelerated life testing models.

Findings

Developed approximation techniques for marginal likelihoods

Applied methods to dual-stress accelerated life testing models

Enhanced model comparison accuracy in Bayesian frameworks

Abstract

In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models.