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Type: Journal article
Title: Modeling individual differences using Dirichlet processes
Author: Navarro, D.
Griffiths, T.
Steyvers, M.
Lee, M.
Citation: Journal of Mathematical Psychology, 2006; 50(2):10-122
Publisher: Academic Press Inc Elsevier Science
Issue Date: 2006
ISSN: 0022-2496
Statement of
Daniel J. Navarro, Thomas L. Griffiths, Mark Steyvers and Michael D. Lee
Abstract: 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.
Keywords: Individual differences; Dirichlet processes; Bayesian nonparametrics
Description: Copyright © 2005 Elsevier Inc. All rights reserved.
RMID: 0020062492
DOI: 10.1016/
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Appears in Collections:Psychology publications
Environment Institute publications

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