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Journal Articles Statistics and Computing Year : 2020

Variance reduction for Markov chains with application to MCMC

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Abstract

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.
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Dates and versions

hal-03033158 , version 1 (01-12-2020)

Identifiers

  • HAL Id : hal-03033158 , version 1

Cite

D Belomestny, L Iosipoi, E Moulines, A Naumov, S Samsonov. Variance reduction for Markov chains with application to MCMC. Statistics and Computing, 2020. ⟨hal-03033158⟩
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