Efficient Monte Carlo sampling by parallel marginalization

Jonathan Weare

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)12657-12662
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number31
StatePublished - Jul 31 2007


  • Filtering
  • Markov chain Monte Carlo
  • Multi-grid
  • Parameter estimation
  • Renormalization

ASJC Scopus subject areas

  • General


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