Assessing Bayes Factor Surfaces Using Interactive Visualization and Computer Surrogate Modeling

Christopher T. Franck and Robert B. Gramacy

The American Statistician, 2020, vol. 74, issue 4, 359-369

Abstract: Bayesian model selection provides a natural alternative to classical hypothesis testing based on p-values. While many articles mention that Bayesian model selection can be sensitive to prior specification on parameters, there are few practical strategies to assess and report this sensitivity. This article has two goals. First, we aim to educate the broader statistical community about the extent of potential sensitivity through visualization of the Bayes factor surface. The Bayes factor surface shows the value a Bayes factor takes as a function of user-specified hyperparameters. Second, we suggest surrogate modeling via Gaussian processes to visualize the Bayes factor surface in situations where computation is expensive. We provide three examples including an interactive R shiny application that explores a simple regression problem, a hierarchical linear model selection exercise, and finally surrogate modeling via Gaussian processes to a study of the influence of outliers in empirical finance. We suggest Bayes factor surfaces are valuable for scientific reporting since they (i) increase transparency by making instability in Bayes factors easy to visualize, (ii) generalize to simple and complicated examples, and (iii) provide a path for researchers to assess the impact of prior choice on modeling decisions in a wide variety of research areas. Supplementary materials for this article are available online.

Date: 2020
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DOI: 10.1080/00031305.2019.1671219

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