Modeling individual differences using Dirichlet processes

Daniel J. Navarro, Thomas L. Griffiths, Mark Steyvers, Michael D. Lee

Research output: Contribution to journalArticlepeer-review

101 Scopus citations


We introduce a Bayesian framework for modeling individual differences, in which subjects are assumed to belong to one of a potentially infinite number of groups. In this model, the groups observed in any particular data set are not viewed as a fixed set that fully explains the variation between individuals, but rather as representatives of a latent, arbitrarily rich structure. As more people are seen, and more details about the individual differences are revealed, the number of inferred groups is allowed to grow. We use the Dirichlet process - A distribution widely used in nonparametric Bayesian statistics - To define a prior for the model, allowing us to learn flexible parameter distributions without overfitting the data, or requiring the complex computations typically required for determining the dimensionality of a model. As an initial demonstration of the approach, we present three applications that analyze the individual differences in category learning, choice of publication outlets, and web-browsing behavior.

Original languageEnglish (US)
Pages (from-to)101-122
Number of pages22
JournalJournal of Mathematical Psychology
Issue number2
StatePublished - Apr 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics


  • Bayesian nonparametrics
  • Dirichlet processes
  • Individual differences


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