  To check the accuracy of Bayesian computations, it is common to use
rank-based simulation-based calibration (SBC). However, SBC has drawbacks: The
test statistic is somewhat ad-hoc, interactions are difficult to examine,
multiple testing is a challenge, and the resulting p-value is not a divergence
metric. We propose to replace the marginal rank test with a flexible
classification approach that learns test statistics from data. This measure
typically has a higher statistical power than the SBC rank test and returns an
interpretable divergence measure of miscalibration, computed from
classification accuracy. This approach can be used with different data
generating processes to address likelihood-free inference or traditional
inference methods like Markov chain Monte Carlo or variational inference. We
illustrate an automated implementation using neural networks and
statistically-inspired features, and validate the method with numerical and
real data experiments.
