Particle filtering with path sampling and an application to a bimodal ocean current model

Jonathan Weare

Research output: Contribution to journalArticlepeer-review


This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.

Original languageEnglish (US)
Pages (from-to)4312-4331
Number of pages20
JournalJournal of Computational Physics
Issue number12
StatePublished - Jul 1 2009


  • Hybrid Monte Carlo
  • Kuroshio
  • Parallel marginalization
  • Particle filter
  • Path sampling
  • Weather prediction

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics


Dive into the research topics of 'Particle filtering with path sampling and an application to a bimodal ocean current model'. Together they form a unique fingerprint.

Cite this