Metropolis Monte Carlo sampling: convergence, localization transition and optimality

TL;DR

This paper investigates the convergence behavior of Metropolis Monte Carlo algorithms, revealing a localization transition affecting the asymptotic distribution shape and linking relaxation dynamics to diffusion and rejection rates.

Contribution

It introduces an analytical and numerical study of localization transitions in Metropolis algorithms, highlighting their impact on convergence and distribution shape.

Findings

Localization transition depends on jump length

Asymptotic distribution shape changes at transition

Relaxation is limited by diffusion and rejection rates

Abstract

Among random sampling methods, Markov Chain Monte Carlo algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties towards the steady state, within a random walk Metropolis scheme. Analysing the relaxation properties of some model algorithms sufficiently simple to enable analytic progress, we show that the deviations from the target steady-state distribution can feature a localization transition as a function of the characteristic length of the attempted jumps defining the random walk. While the iteration of the Monte Carlo algorithm converges to equilibrium for all choices of jump parameters, the localization transition changes drastically the asymptotic shape of the difference between the probability distribution reached after a finite number of steps of the algorithm and the target equilibrium distribution. We argue that the relaxation before and after the localisation transition is respectively limited by diffusion and rejection rates.