Local-mass preserving prior distributions for nonparametric bayesian models

Juhee Lee, Steven N. MacEachern, Yiling Lu, Gordon B. Mills

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We address the problem of prior specification for models involving the two-parameter Poisson-Dirichlet process. These models are sometimes partially subjectively specified and are always partially (or fully) specified by a rule. We develop prior distributions based on local mass preservation. The robustness of posterior inference to an arbitrary choice of overdispersion under the proposed and current priors is investigated. Two examples are provided to demonstrate the properties of the proposed priors. We focus on the three major types of inference: clustering of the parameters of interest, estimation and prediction. The new priors are found to provide more stable inference about clustering than traditional priors while showing few drawbacks. Furthermore, it is shown that more stable clustering results in more stable inference for estimation and prediction. We recommend the local-mass preserving priors as a replacement for the traditional priors.

Original languageEnglish (US)
Pages (from-to)307-330
Number of pages24
JournalBayesian Analysis
Issue number2
StatePublished - 2014
Externally publishedYes


  • Clustering
  • Dirichlet process
  • Local mass
  • Nonparametric bayes
  • Prior misspecification
  • Two-parameter poisson-dirichlet process

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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