Bayes factors for accelerated life testing models

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.

• Publication year

  2021

• Venue

  Communications for Statistical Applications and Methods

• Publication date

  2021-11-18

• Fields of study

  Mathematics

• Identifiers
  DOI 10.29220/CSAM.2022.29.5.513arXiv 2111.09593
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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