Dirichlet Process Models
Dirichlet Process Models is a Bayesian nonparametric model that employs a Dirichlet process as a prior distribution over probability distributions, enabling flexible clustering and mixture modeling with an unbounded number of components.
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Publications
79.2K
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1.9K
Authors
801
Institutions
Publications per year
2017–2026
204
Variants
Dirichlet Process ModelDpm
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A Bayesian Analysis of Some Nonparametric Problems
Thomas S. Ferguson · The Annals of Statistics · 1973 · 4.8K citations · Full text
Hierarchical Dirichlet Processes
Yee Whye Teh, Michael I. Jordan, Matthew J. Beal et al. · Journal of the American Statistical Association · 2006 · 3.6K citations
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Duke University
Durham, United States
33 h-index
128 publications
12.2K citations
University of California, Berkeley
Berkeley, United States
25 h-index
54 publications
8.1K citations
University of Cambridge
Cambridge, United Kingdom
19 h-index
53 publications
3.7K citations
Massachusetts Institute of Technology
Cambridge, United States
18 h-index
53 publications
4.2K citations
Carnegie Mellon University
Pittsburgh, United States
18 h-index
33 publications
5.3K citations
Princeton University
Princeton, United States
17 h-index
29 publications
4.2K citations

Concordia University
Montreal, Canada
17 h-index
54 publications
1.5K citations

Centre National de la Recherche Scientifique
Paris, France
16 h-index
43 publications
5.7K citations
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60 publications · 13K citations
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32 publications · 10.4K citations
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Repository · 54 publications · 1.7K citations
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23 publications · 2.5K citations
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21 publications · 903 citations
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