Random Partition Models
Random Partition Models is a class of Bayesian nonparametric models that induce a probability distribution over the space of all possible partitions of a set of data points, distinguished by their use of stochastic processes—such as the Chinese Restaurant Process or Pitman-Yor process—to define the partition structure in a way that allows the number of clusters to grow with the data.
65
Publications
3.9K
Citations
119
Authors
88
Institutions
Publications per year
2017–2026
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The University of Texas MD Anderson Cancer Center, United States
4 publications · 265 citations
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3
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3
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1–5 of 119
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Pontificia Universidad Católica de Chile
Santiago, Chile
5 h-index
6 publications
387 citations
University of California, Berkeley
Berkeley, United States
5 h-index
11 publications
1.2K citations
The University of Texas at Austin
Austin, United States
4 h-index
4 publications
114 citations

University of Minnesota
Minneapolis, United States
3 h-index
4 publications
211 citations
Brigham Young University
Provo, United States
3 h-index
5 publications
182 citations
The Ohio State University
Columbus, United States
3 h-index
4 publications
75 citations
North Carolina State University
Raleigh, United States
2 h-index
3 publications
129 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
2 publications
100 citations
1–8 of 88
JCGS · 3 publications · 202 citations
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3 publications · 140 citations
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2 publications · 76 citations
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2 publications · 34 citations
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2 publications · 35 citations
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