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

Bayes factors for accelerated life testing models