Abstract

Model assessment and model comparison are a crucial part of statistical analysis. Due to recent computational advances, sophisticated techniques for Bayesian model assessment are becoming increasingly popular. There is a rich literature on Bayesian methods for model assessment and model comparison, including [1, 3, 6, 9, 10, 13, 14, 16, 17, 18, 24, 26, 28, 30, 32, 33, 34, 36]. The scope of Bayesian model assessment can be investigated via model diagnostics, goodness of fit measures, or posterior model probabilities (or Bayes factors). A comprehensive account of model diagnostics and related methods for model assessment is given in [15].

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