Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This challenge is particularly salient in black box inference methods, which can hide details and obscure inference failures. In this work, we extend the recently introduced bidirectional Monte Carlo technique to evaluate MCMC-based posterior inference algorithms. By running annealed importance sampling (AIS) chains both from prior to posterior and vice versa on simulated data, we upper bound in expectation the symmetrized KL divergence between the true posterior distribution and the distribution of approximate samples. We present Bounding Divergences with REverse Annealing (BREAD), a protocol for validating the relevance of simulated data experiments to real datasets, and integrate it into two probabilistic programming languages: WebPPL and Stan. As an example of how BREAD can be used to guide the design of inference algorithms, we apply it to study the effectiveness of different model representations in both WebPPL and Stan.

通过在模拟数据上运行双向蒙特卡洛技术，我们扩展了近来引入的双向蒙特卡洛技巧来评估基于MCMC的后验推理算法，并提出了一种验证模拟数据实验与真实数据集相关性的协议BREAD，并将其整合到WebPPL和Stan两种概率编程语言中。

使用双向蒙特卡罗法（Bidirectional Monte Carlo）测量MCMC推断的可靠性