Particle learning and smoothing

Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, Nicholas G. Polson

Research output: Contribution to journalArticlepeer-review

214 Scopus citations


Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.

Original languageEnglish (US)
Pages (from-to)88-106
Number of pages19
JournalStatistical Science
Issue number1
StatePublished - 2010
Externally publishedYes


  • Mixture kalman filter
  • Parameter learning
  • Particle learning
  • Sequential inference
  • Smoothing
  • State filtering
  • State space models

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Statistics, Probability and Uncertainty


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