Hierarchical Dirichlet processes

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Hierarchical Dirichlet processes (2004)

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

Venue:	Journal of the American Statistical Association
Citations:	915 - 78 self

BibTeX

@ARTICLE{Teh04hierarchicaldirichlet,
    author = {Yee Whye Teh and Michael I. Jordan and Matthew J. Beal and David M. Blei},
    title = {Hierarchical Dirichlet processes},
    journal = {Journal of the American Statistical Association},
    year = {2004},
    volume = {101}
}

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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

Keyphrases

mixture model hierarchical dirichlet process dirichlet process child dirichlet share mixture component base measure mixture component different group lancelot james helpful discussion various group share atom jim pitman hierarchical dirichlet hierarchical model useful comment nonparametric prior

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