Hierarchical Dirichlet Processes

Hierarchical Dirichlet Processes is a Bayesian nonparametric model that extends the Dirichlet process by organizing multiple Dirichlet processes in a hierarchical structure, enabling shared latent clusters across related groups through a global measure that serves as the base measure for group-specific Dirichlet processes.

239

Publications

20.9K

Citations

560

Authors

257

Institutions

Publications per year

2017–2026

63

Authors

560

  1. DM
    David M. Blei

    Princeton University, United States

    17 publications · 6.8K citations

    17

    h-index

  2. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    12 publications · 5.5K citations

    12

    h-index

  3. EB
    Erik B. Sudderth

    Massachusetts Institute of Technology, United States

    8 publications · 676 citations

    8

    h-index

  4. LC
    Lawrence Carin

    Duke University, United States

    7 publications · 517 citations

    7

    h-index

  5. JP
    John Paisley

    Columbia University, United States

    7 publications · 2.2K citations

    7

    h-index

1–5 of 560

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 828 citations

    11

    h-index

  2. 8

    h-index

  3. Journal of the American Statistical Association

    7 publications · 5.4K citations

    7

    h-index

  4. IEEE Transactions on Signal Processing

    5 publications · 366 citations

    5

    h-index

  5. PLoS Computational Biology

    4 publications · 393 citations

    4

    h-index

1–5