Hierarchical Dirichlet processes

Hierarchical Dirichlet processes (2004)

Cached

Download Links

by Yee Whye Teh , Michael I. Jordan , Matthew J. Beal , David M. Blei

Venue:	Journal of the American Statistical Association
Citations:	328 - 44 self

Versions

Version History

Metadata Version 1

Datum	Value	Source
TITLE	Hierarchical Dirichlet processes	INFERENCE
AUTHOR NAME	Yee Whye Teh	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, National University of Singapore,	SVM HeaderParse 0.2
AUTHOR ADDR	Singapore 117543	SVM HeaderParse 0.2
AUTHOR NAME	Michael I. Jordan	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science Division and Department of Statistics,; University of California at Berkeley, Berkeley CA 94720-1776	SVM HeaderParse 0.2
AUTHOR ADDR	USA	SVM HeaderParse 0.2
AUTHOR NAME	Matthew J. Beal	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science & Engineering,; State University of New York at Buffalo, Buffalo NY 14260-2000	SVM HeaderParse 0.2
AUTHOR ADDR	USA	SVM HeaderParse 0.2
AUTHOR NAME	David M. Blei	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Computer Science, Princeton University,; ∗ Yee Whye Teh is Lee Kuan Yew Postdoctoral Fellow, Department of Computer Science, National University of; Professor of Computer Science, Princeton University	SVM HeaderParse 0.2
AUTHOR ADDR	; Princeton, NJ 08544, USA; Singapore, Singapore; Micharl I. Jordan is Professor of Computer Science and Statistics, UC Berkeley, CA; Matthew J.	SVM HeaderParse 0.2
ABSTRACT	program. The authors wish to acknowledge helpful discussions with Lancelot James and Jim Pitman and the referees for useful comments. 1 We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessar-ily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	Journal of the American Statistical Association	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
VOLUME	101	INFERENCE
TECH	Technical Report 653	INFERENCE
CITATIONS	42 found	ParsCit 1.0