Accelerating MCMC by Rare Intermittent Resets

Abstract

We propose a scheme for accelerating Markov Chain Monte Carlo by introducing random resets that become increasingly rare in a precise sense. We show that this still leads to the desired asymptotic average and establish an associated concentration bound. We show by numerical experiments that this scheme can be used to advantage in order to accelerate convergence by a judicious choice of the resetting mechanism.