Efficient Monte Carlo sampling by parallel marginalization
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Communicated by Alexandre J. Chorin, University of California, Berkeley, CA, June 8, 2007 (received for review January 12, 2007)
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.
Footnotes
- †E-mail: weare{at}math.berkeley.edu
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Author contributions: J.W. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.
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The author declares no conflict of interest.
- Abbreviation:
- MCMC,
- Markov chain Monte Carlo.
- © 2007 by The National Academy of Sciences of the USA
