Ensemble preconditioning for Markov chain Monte Carlo simulation

Benedict Leimkuhler, Charles Matthews, Jonathan Weare

Research output: Contribution to journalArticle

Abstract

We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighbouring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics, thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable.

Original languageEnglish (US)
Pages (from-to)277-290
Number of pages14
JournalStatistics and Computing
Volume28
Issue number2
DOIs
StatePublished - Mar 1 2018

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Keywords

  • BFGS
  • Brownian dynamics
  • Computational statistics
  • Langevin methods
  • Machine learning
  • Markov chain Monte Carlo
  • MCMC
  • Stochastic sampling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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