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。