Jon D. McAuliffe David M. Blei Michael I. Jordan October 4, 2004  Home/Search
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Abstract: The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data come in. However, the behavior of the model is sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G0 . Most previous applications have either fixed G0 as a member of a parametric family or treated G0 in a Bayesian fashion, using parametric prior specifications. In contrast, we have ... (Update)
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BibTeX entry: (Update)
@misc{ david-nonparametric,
author = "Jon Mcauliffe David",
title = "Nonparametric empirical Bayes for the Dirichlet process mixture model",
url = "citeseer.ist.psu.edu/756443.html" }
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1 A Bayesian semiparametric joint hierarchical model for longi.. (context) - Brown, Ibrahim - 2003
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