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

Authors

902

  1. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    14 publications · 2.7K citations

    13

    h-index

  2. NB
    Nizar Bouguila

    Concordia University, Canada

    15 publications · 499 citations

    13

    h-index

  3. DB
    David B. Dunson

    Duke University, United States

    12 publications · 706 citations

    11

    h-index

  4. WF
    Wentao Fan

    Huaqiao University, China

    9 publications · 194 citations

    9

    h-index

  5. DM
    David M. Blei

    Princeton University, United States

    8 publications · 2.3K citations

    8

    h-index

1–5 of 902

Venues

  1. Journal of the American Statistical Association

    26 publications · 4.5K citations

    22

    h-index

  2. arXiv (Cornell University)

    Repository · 25 publications · 1.1K citations

    17

    h-index

  3. Bayesian Analysis

    14 publications · 2.1K citations

    12

    h-index

  4. Biometrics

    11 publications · 327 citations

    11

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 13 publications · 4.3K citations

    11

    h-index

1–5