Particle learning for general mixtures

Carlos M. Carvalho, Hedibert F. Lopes, Nicholas G. Polson, Matt A. Taddy

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


This paper develops particle learning (PL) methods for the estimation of general mixture models. The approach is distinguished from alternative particle filtering methods in two major ways. First, each iteration begins by resampling particles according to posterior predictive probability, leading to a more efficient set for propagation. Second, each particle tracks only the "essential state vector" thus leading to reduced dimensional inference. In addition, we describe how the approach will apply to more general mixture models of current interest in the literature; it is hoped that this will inspire a greater number of researchers to adopt sequential Monte Carlo methods for fitting their sophisticated mixture based models. Finally, we show that PL leads to straightforward tools for marginal likelihood calculation and posterior cluster allocation.

Original languageEnglish (US)
Pages (from-to)709-740
Number of pages32
JournalBayesian Analysis
Issue number4
StatePublished - 2010
Externally publishedYes


  • Dirichlet process
  • Indian buffet process
  • Mixture models
  • Nonparametric
  • Particle filtering
  • Probit stick-breaking

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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