Variational formulas for asymptotic variance of general Markov processes

Abstract

The asymptotic variance is an important criterion to evaluate the performance of Markov processes, especially for the central limit theorems. In this talk, we give a variational formula for the asymptotic variance of (nonreversible) Markov processes. The variational formula provides many applications, extending the classical Peskun’s comparison theorem to non-reversible Markov processes, and obtaining several comparison theorems between Markov processes with various perturbations. Based on joint work with Yong-Hua Mao.

Speaker

黄璐静，福建师范大学数学与统计学院副教授。2018年博士毕业于北京师范大学，主要研究方向为马氏过程平稳性。