arXiv 1802.025382018-02stat.MLstat.CO

是的，但它起作用了吗？评价变分推断

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

备注：: Appearing at International Conference on Machine Learning 2018
发表信息：: Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5581-5590, 2018. http://proceedings.mlr.press/v80/yao18a.html

摘要：

虽然总是可以计算后验分布的变分近似，但很难发现这种近似的问题。我们提出了两个诊断算法来缓解这个问题。Pareto平滑重要性抽样（PSIS）诊断为联合分布提供了拟合优度测量，同时改善了估计中的误差。基于变分模拟的校准（VSBC）评估点估计的平均性能。

英文摘要：

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulation-based calibration (VSBC) assesses the average performance of point estimates.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。