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.

翻译：在Bayesian加速寿命测试中,最常用的模型比较工具是偏差信息标准,另一种更正式的替代办法是使用Bayes系数来比较模型,然而,Bayesian加速寿命测试模型有一个以上压力器,往往有数学上难以解决的后继物分布和Markov连锁Monte Carlo方法,以获得后继物样本作为推理依据。在与这些复杂模型合作时,对边际可能性的计算具有挑战性。本文探讨了在加速生命测试模式中接近边际可能性及其在加速生命测试模式中的应用的方法。本文探讨了双重压力模型。