David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten
TL;DR该论文提出了一种新的框架,将一类分布估计算法,特别是协方差矩阵适应算法,写成了蒙特卡罗期望最大化算法和无限样本极限下的精确 EM,这个发现为 EDAs 研究提供了一个基于 EM 坚实统计基础的新的、一致的框架。
Abstract
We show that under mild conditions, estimation of distribution algorithms (EDAs) can be written as variational Expectation-Maximization (EM) that uses a mixture of weighted particles as the approximate posterior. In the infinite particle limit, EDAs can be viewed as exact EM. Because E