Particle Learning for Sequential Bayesian Computation

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

Research output: Chapter in Book/Report/Conference proceedingChapter


Particle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive distribution and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.

Original languageEnglish (US)
Title of host publicationBayesian Statistics 9
PublisherOxford University Press
ISBN (Electronic)9780191731921
ISBN (Print)9780199694587
StatePublished - Jan 19 2012
Externally publishedYes


  • Bayesian
  • Conditional dynamic linear models
  • Dirichlet
  • Dynamic factor models
  • Essential state vector
  • Mixture models
  • Nonparametric
  • Particle learning
  • Sequential inference

ASJC Scopus subject areas

  • Mathematics(all)


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