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.

翻译：为检验贝叶斯计算的准确性，常用基于秩的仿真校准方法（SBC）。然而SBC存在不足：检验统计量具有一定随意性，交互作用难以检验，多重比较存在挑战，且所得p值并非散度度量指标。我们提出用灵活的分类方法替代边缘秩检验，该方法可从数据中学习检验统计量。该度量通常比SBC秩检验具有更高的统计效能，并能从分类准确率中计算具有可解释性的误校准散度度量。该方法可适配不同的数据生成过程，用于处理无似然推断或马尔可夫链蒙特卡洛、变分推断等传统推断方法。我们展示了基于神经网络和统计启发特征的自动化实现，并通过数值实验与真实数据实验对该方法进行了验证。