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Adaptive Metropolis sampling and optimization with product distributions

By David H. Wolpert and Chiu Fan Lee

Abstract

The Metropolis-Hastings (MH) algorithm is a way to IID sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x ′) to generate a random walk {x(t)} which converges to a set of samples of π. Here, we introduce a T-updating phase after the cooling period and before sampling begins. In the updating phase, {x(t)} is used to update T at t and our update method corresponds to the information-theoretically optimal meanfield approximation to π. 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

Year: 2005
OAI identifier: oai:CiteSeerX.psu:10.1.1.225.5857
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