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.

1.1K

Publications

79.2K

Citations

1.9K

Authors

801

Institutions

Publications per year

2017–2026

204

Variants

Dirichlet Process ModelDpm

Authors

1.9K

  1. DB
    David B. Dunson

    Duke University, United States

    29 publications · 1.8K citations

    20

    h-index

  2. NB
    Nizar Bouguila

    Concordia University, Canada

    34 publications · 1.1K citations

    18

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    22 publications · 7.4K citations

    18

    h-index

  4. DM
    David M. Blei

    Princeton University, United States

    17 publications · 8.3K citations

    17

    h-index

  5. AL
    Antonio Lijoi

    University of Pavia, Italy

    16 publications · 846 citations

    15

    h-index

1–5 of 1.9K

Venues

  1. Journal of the American Statistical Association

    60 publications · 13K citations

    35

    h-index

  2. The Annals of Statistics

    32 publications · 10.4K citations

    26

    h-index

  3. arXiv (Cornell University)

    Repository · 54 publications · 1.7K citations

    23

    h-index

  4. Bayesian Analysis

    23 publications · 2.5K citations

    20

    h-index

  5. Biometrics

    21 publications · 903 citations

    18

    h-index

1–5