Dirichlet Process Mixtures
Dirichlet Process Mixtures is a Bayesian nonparametric model that employs a Dirichlet process prior to define a flexible mixture distribution over an infinite number of potential components, enabling data-driven determination of cluster structure.
434
Publications
31.4K
Citations
902
Authors
426
Institutions
Publications per year
2017–2026
110
Variants
Dirichlet Process Mixture ModelsDpmms
434
Variational inference for Dirichlet process mixtures
David M. Blei, Michael I. Jordan · Bayesian Analysis · 2006 · 1.5K citations · Full text
The value of prior knowledge in discovering motifs with MEME.
Trisha L. Bailey, Charles Elkan · PubMed · 1995 · 681 citations
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Duke University
Durham, United States
21 h-index
48 publications
5.3K citations
Texas A&M University
College Station, United States
15 h-index
34 publications
1.3K citations
University of California, Berkeley
Berkeley, United States
13 h-index
30 publications
4.1K citations
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
28 publications
1.7K citations

Concordia University
Montreal, Canada
12 h-index
23 publications
665 citations
University of Cambridge
Cambridge, United Kingdom
12 h-index
31 publications
2.8K citations
University of California, Santa Cruz
Santa Cruz, United States
11 h-index
18 publications
2.6K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
14 publications
2.4K citations
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26 publications · 4.5K citations
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Repository · 25 publications · 1.1K citations
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14 publications · 2.1K citations
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11 publications · 327 citations
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JCGS · 13 publications · 4.3K citations
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