Variance reduction for Markov chains with application to MCMC

In this paper, we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce the overall finite sample variance. This feature is theoretically demonstrated by means of a deep non-asymptotic analysis of a variance reduced functional as well as by a thorough simulation study. In particular, we apply our method to various MCMC Bayesian estimation problems where it favorably compares to the existing variance reduction approaches.