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。

使用基于模拟校准方法验证贝叶斯推断算法

Validating Bayesian Inference Algorithms with Simulation-Based  Calibration

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

Measuring the reliability of MCMC inference with bidirectional Monte  Carlo

We present further development and the first public release of our multimodal
nested sampling algorithm, called MultiNest. This Bayesian inference tool
calculates the evidence, with an associated error estimate, and produces
posterior samples from distributions that may contain multiple modes and
pronounced (curving) degeneracies in high dimensions. The developments
presented here lead to further substantial improvements in sampling efficiency
and robustness, as compared to the original algorithm presented in Feroz &
Hobson (2008), which itself significantly outperformed existing MCMC techniques
in a wide range of astrophysical inference problems. The accuracy and economy
of the MultiNest algorithm is demonstrated by application to two toy problems
and to a cosmological inference problem focussing on the extension of the
vanilla $\Lambda$CDM model to include spatial curvature and a varying equation
of state for dark energy. The MultiNest software, which is fully parallelized
using MPI and includes an interface to CosmoMC, is available at
this http URL It will also be released as part
of the SuperBayeS package, for the analysis of supersymmetric theories of
particle physics, at this http URL

介绍了 MultiNest 算法，它是一种用于贝叶斯推断和后验采样的多模态和高度相关性分布的算法，并进行了对该算法的应用和测评。