Open Access
March 2018 Approximation of Bayesian Predictive p-Values with Regression ABC
David J. Nott, Christopher C. Drovandi, Kerrie Mengersen, Michael Evans
Bayesian Anal. 13(1): 59-83 (March 2018). DOI: 10.1214/16-BA1033


In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. The second problem considered is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several examples.


Download Citation

David J. Nott. Christopher C. Drovandi. Kerrie Mengersen. Michael Evans. "Approximation of Bayesian Predictive p-Values with Regression ABC." Bayesian Anal. 13 (1) 59 - 83, March 2018.


Published: March 2018
First available in Project Euclid: 16 November 2016

zbMATH: 06873718
MathSciNet: MR3737943
Digital Object Identifier: 10.1214/16-BA1033

Keywords: ABC , Bayesian inference , Bayesian p-values , posterior predictive check , Prior predictive check , weakly informative prior

Vol.13 • No. 1 • March 2018
Back to Top