Efficient Monte Carlo sampling by parallel marginalization

Jonathan Weare

doi:10.1073/pnas.0705418104

2007-07-31

Jonathan Weare *

DOI：10.1073/pnas.0705418104

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摘要

En 中文

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.

Keyword:

Markov chain Monte Carlo

renormalization

multi-grid

filtering

parameter estimation

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期刊

Proceedings of the National Academy of Sciences of the United States of America

IF:

9.1

论文数:

10.8W

被引数:

73.5W

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