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

8

Authors

119

  1. FA
    Fernando A. Quintana

    Pontificia Universidad Católica de Chile, Chile

    5 publications · 256 citations

    5

    h-index

  2. AO
    Andreĭ Okounkov

    Princeton University, United States

    4 publications · 402 citations

    4

    h-index

  3. PM
    Peter Müller

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 265 citations

    4

    h-index

  4. BP
    Boris Pittel

    The Ohio State University, United States

    3 publications · 65 citations

    3

    h-index

  5. JP
    Jim Pitman

    University of California, Berkeley, United States

    3 publications · 669 citations

    3

    h-index

1–5 of 119

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 202 citations

    3

    h-index

  2. 3

    h-index

  3. The Annals of Applied Probability

    2 publications · 76 citations

    2

    h-index

  4. Statistics in Medicine

    2 publications · 34 citations

    2

    h-index

  5. The Electronic Journal of Combinatorics

    2 publications · 35 citations

    2

    h-index

1–5