Abstract #301121


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JSM 2002 Abstract #301121
Activity Number: 147
Type: Contributed
Date/Time: Monday, August 12, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #301121
Title: Modal Clustering in One-Dimensional, Conjugate Dirichlet Process Mixture Models
Author(s): David Dahl*+
Affiliation(s): University of Wisconsin, Madison
Address: 906 Eagle Heights Apt. F, Madison, Wisconsin, 53705, U.S.A.
Keywords: Perfect sampling ; Clustering ; Dirichlet process ; Product partition model ; Coupling ; Model-based clustering
Abstract:

The Dirichlet process mixture (DPM) model is a popular nonparametric Bayesian tool for modeling unknown distributions through mixtures of components. Integrating out the latent location variables leads to a product partition model for clustering observations. This paper describes a dynamic programming algorithm for univariate data which quickly finds either the maximizer of the posterior clustering distribution or the maximizer of the clustering likelihood. Applications of the algorithm are shown. The algorithm plays a key role in a coupling-from-the-past procedure for obtaining exact draws from a conjugate DPM model with a small number of observations. On a much larger scale, the algorithm is used as a clustering technique for a microarray dataset of over 10,000 genes.


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