变分提升：迭代优化后验近似

Nov, 2016

Variational Boosting: Iteratively Refining Posterior Approximations

Andrew C. Miller, Nicholas Foti, Ryan P. Adams

TL;DR本文提出了一种黑盒变分推理方法——变分 boosting,通过迭代优化来逼近一个越来越丰富的逼近类，从而扩展其变分逼近类，应用于合成和真实的统计模型，表明通过比较精确和有效地后验推理，其结果优于现有的后验逼近算法。

Abstract

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing