Notes on Using Control Variates for Estimation with Reversible MCMC Samplers

A general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients of the optimal linear combination of the control variates are computed, and adaptive, consistent MCMC estimators are derived for these optimal coefficients. All methodological and asymptotic arguments are rigorously justified. Numerous MCMC simulation examples from Bayesian inference applications demonstrate that the resulting variance reduction can be quite dramatic.

💡 Research Summary

The paper develops a comprehensive framework for incorporating control variates into reversible Markov chain Monte Carlo (MCMC) samplers with the goal of dramatically reducing estimator variance. Starting from the observation that, for a reversible chain with transition kernel P and invariant distribution π, any function h of the form h = g – Pg has zero mean under π, the authors construct a control‑variates estimator μ̂_n^β = (1/n)∑_{i=1}^n