Variance reduction for Markov chains with application to MCMC

Autor:	Belomestny, D., Iosipoi, L., Moulines, E., Naumov, A., Samsonov, S.
Rok vydání:	2019
Předmět:	Mathematics - Statistics Theory Computer Science - Machine Learning Mathematics - Probability Statistics - Computation Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 favourably compares to the existing variance reduction approaches.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.03643 Zobrazit plný text záznamu View this record from Arxiv