Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
复制标题
DOI：
--
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Sean Talts;M. Betancourt;Daniel P. Simpson;Aki Vehtari;A. Gelman
中科院分区：
文献类型：
--
作者：
Sean Talts;M. Betancourt;Daniel P. Simpson;Aki Vehtari;A. Gelman
验证贝叶斯计算的正确性是一个挑战。对于实践中常见的复杂模型尤其如此，因为这些模型需要复杂的模型实现和算法。在本文中，我们介绍了\emph{基于模拟的校准}（SBC），这是一种验证能够产生后验样本的贝叶斯算法推断的通用程序。此过程不仅识别模型实现中的不准确计算和不一致，而且还提供图形摘要，可以指示出现的问题的性质。我们认为，SBC是稳健的贝叶斯工作流的关键部分，也是那些开发计算算法和统计软件的有用工具。
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.