A method for combining inference across related nonparametric Bayesian models

Peter Müller, Fernando Quintana, Gary Rosner

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparametric model. For the special case of Dirichlet process mixtures, we develop a Markov chain Monte Carlo scheme to allow efficient implementation of full posterior inference in the given model.

Original languageEnglish (US)
Pages (from-to)735-749
Number of pages15
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number3
StatePublished - 2004
Externally publishedYes


  • Dependence
  • Dirichlet process
  • Hierarchical model
  • Meta-analysis
  • Random functions

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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