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To check the accuracy of Bayesian computations, it is common to userank-based simulation-based calibration (SBC). However, SBC has drawbacks: Thetest statistic is somewhat ad-hoc, interactions are difficult to examine,multiple testing is a challenge, and the resulting p-value is not a divergencemetric. We propose to replace the marginal rank test with a flexibleclassification approach that learns test statistics from data. This measuretypically has a higher statistical power than the SBC rank test and returns aninterpretable divergence measure of miscalibration, computed fromclassification accuracy. This approach can be used with different datagenerating processes to address likelihood-free inference or traditionalinference methods like Markov chain Monte Carlo or variational inference. Weillustrate an automated implementation using neural networks andstatistically-inspired features, and validate the method with numerical andreal data experiments.

Discriminative calibration: Check Bayesian computation from simulations and flexible classifier