Sequential monte carlo methods for dynamic systems

Jun S. Liu, Rong Chen

Research output: Contribution to journalArticlepeer-review

1495 Scopus citations

Abstract

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao–Blackwellization and to compare different Monte Carlo procedures.

Original languageEnglish (US)
Pages (from-to)1032-1044
Number of pages13
JournalJournal of the American Statistical Association
Volume93
Issue number443
DOIs
StatePublished - Sep 1 1998
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Blind deconvolution
  • Bootstrap filter
  • Gibbs sampling
  • Hidden Markov model
  • Kalman filter
  • Markov chain Monte Carlo
  • Particle filter
  • Sequential imputation
  • State-space model
  • Target tracking

Fingerprint Dive into the research topics of 'Sequential monte carlo methods for dynamic systems'. Together they form a unique fingerprint.

Cite this