Does waste-recycling really improve Metropolis-Hastings Monte Carlo algorithm?

TL;DR

This paper investigates the waste-recycling Monte Carlo method's effectiveness in improving the Metropolis-Hastings algorithm, revealing that it can sometimes increase variance but is beneficial in specific cases like the Boltzmann algorithm.

Contribution

The paper extends waste-recycling Monte Carlo into a control variate framework and identifies conditions where it outperforms standard Metropolis-Hastings.

Findings

WR can have higher asymptotic variance than MH in general

WR improves performance for the Boltzmann Metropolis-Hastings algorithm

Optimal control variate choice minimizes asymptotic variance

Abstract

The Metropolis Hastings algorithm and its multi-proposal extensions are aimed at the computation of the expectation $<\pi,f>$ of a function $f$ under a probability measure $\pi$ difficult to simulate. They consist in constructing by an appropriate acceptation/rejection procedure a Markov chain $(X_k,k\geq 0)$ with transition matrix $P$ such that $\pi$ is reversible with respect to $P$ and in estimating $<\pi,f>$ by the empirical mean $I_n(f)=\inv{n}\sum_{k=1}^n f(X_k)$. The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard Metropolis-Hastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exhibit the optimal choice of the control variate in terms of asymptotic variance. We also give an example which shows that in contradiction to the intuition of physicists, the WR algorithm can have an asymptotic variance larger than the one of the Metropolis-Hastings algorithm. However, in the particular case of the Metropolis-Hastings algorithm called Boltzmann algorithm, we prove that the WR algorithm is asymptotically better than the Metropolis-Hastings algorithm.