Selection of a MCMC simulation strategy via an entropy convergence criterion

TL;DR

This paper introduces an entropy-based criterion for selecting the most efficient MCMC simulation strategy by comparing convergence rates early in the simulation process, using parallel chains and theoretical conditions for consistency.

Contribution

The paper proposes a novel entropy convergence criterion for early comparison of MCMC strategies, supported by theoretical conditions ensuring reliable estimation.

Findings

The entropy criterion effectively identifies the best strategy early in the simulation.

Theoretical conditions guarantee the consistency of the entropy estimate.

Simulated examples demonstrate the criterion's practical utility.

Abstract

In MCMC methods, such as the Metropolis-Hastings (MH) algorithm, the Gibbs sampler, or recent adaptive methods, many different strategies can be proposed, often associated in practice to unknown rates of convergence. In this paper we propose a simulation-based methodology to compare these rates of convergence, grounded on an entropy criterion computed from parallel (i.i.d.) simulated Markov chains coming from each candidate strategy. Our criterion determines on the very first iterations the best strategy among the candidates. Theoretically, we give for the MH algorithm general conditions under which its successive densities satisfy adequate smoothness and tail properties, so that this entropy criterion can be estimated consistently using kernel density estimate and Monte Carlo integration. Simulated examples are provided to illustrate this convergence criterion.