学术报告
A weighted space-filling design using Kullback-Leibler divergence — 孙法省（东北师范大学）
题目： A weighted space-filling design using Kullback-Leibler divergence
报告人：孙法省（东北师范大学）
Abstract : This paper introducse a new way of discrete approximation a continuous probability distribution by minimizing the Kullback-Leibler divergence. The results sample points can be seen as a weight space-filling design in the region of interest. In addition, theoretical results are provided to show that the empirical distribution of these points convergences to the goal distribution. The advantage of these points over MCMC and other deterministic sampling are illustrated in the simulation. Two important applications of such points are then highlighted: (a) simulation from the complex probability densities, and (b) exploration and optimization of expensive black-box functions.
时间：2020年11月12日（周四）下午20:00-21:00
地点：线上腾讯会议（会议号：766 943 031）
联系人：周洁、胡涛
