Modeling Individual Differences with Dirichlet Processes
Thomas L. Griffiths, Michael Lee, Danielle Navarro, Mark Steyvers · eScholarship (California Digital Library) · 2005
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 explain the variation between individuals, but rather as representatives of a latent, arbitrarily rich structure.As more people are seen, the number of observed groups is allowed to grow, as more details about the individual differences are revealed.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 an application of the method to categorization data.