2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/99209We propose an adaptive Metropolis-Hastings algorithm in which sampled data are used to update the proposal distribution. We use the samples found by the algorithm at a particular step to form the information-theoretically optimal mean-field approximation to the target distribution, and update the proposal distribution to be that approximatio. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm in sampling and in annealing optimization.To appear in Europhysics LettersOther Condensed MatterDisordered Systems and Neural Networkstext