Efficient Monte Carlo sampling by parallel marginalization

Abstract

Markov chain Monte Carlo sampling methods often suffer from long correlation times. Consequently, these methods must be run for many steps to generate an independent sample. In this paper a method is proposed to overcome this difficulty. The method utilizes information from rapidly equilibrating coarse Markov chains that sample marginal distributions of the full system. This is accomplished through exchanges between the full chain and the auxiliary coarse chains. Results of numerical tests on the bridge sampling and filtering/smoothing problems for a stochastic differential equation are presented.

7 figures, 2 figures, PNAS .cls and .sty files, submitted to PNAS

References in corpus (1)

• A maximum likelihood algorithm for the estimation and renormalization of exponential densities

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