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.

本文提出了一种使用分类方法代替 SBC 进行 Bayesian 计算的方法，该方法利用学习的测试统计量，通常具有比 SBC 更高的统计功率和可解释的误差度量，并可用于不同数据生成过程的似然函数自由推理或传统的推理方法，例如马尔可夫链蒙特卡罗或变分推理。

区分性校准

Discriminative calibration

Verifying the correctness of Bayesian computation is challenging. This is
especially true for complex models that are common in practice, as these
require sophisticated model implementations and algorithms. In this paper we
introduce \emph{simulation-based calibration} (SBC), a general procedure for
validating inferences from Bayesian algorithms capable of generating posterior
samples. This procedure not only identifies inaccurate computation and
inconsistencies in model implementations but also provides graphical summaries
that can indicate the nature of the problems that arise. We argue that SBC is a
critical part of a robust Bayesian workflow, as well as being a useful tool for
those developing computational algorithms and statistical software.

介绍了一种名为 “基于模拟的校准（SBC）” 的通用过程，可验证使用贝叶斯算法生成后验样本的推断的正确性和一致性，并提供可指示问题性质的图形总结。