Bayesian Dirichlet-multinomial Models

Bayesian Dirichlet-Multinomial Models is a hierarchical Bayesian model that combines the multinomial likelihood of categorical count data with a Dirichlet prior distribution to enable probabilistic inference on category probabilities while accounting for overdispersion and uncertainty in parameter estimation.

52

Publications

3.3K

Citations

138

Authors

96

Institutions

Publications per year

2017–2026

18

Authors

138

  1. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    2 publications · 371 citations

    2

    h-index

  2. MV
    Marina Vannucci

    Rice University, United States

    2 publications · 105 citations

    2

    h-index

  3. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 154 citations

    2

    h-index

  4. MD
    Matthew D. Koslovsky

    The University of Texas Health Science Center at Houston, United States

    2 publications · 33 citations

    2

    h-index

  5. GT
    GL Tian

    1 publications · 24 citations

    1

    h-index

1–5 of 138

Venues

  1. Statistics in Medicine

    4 publications · 205 citations

    4

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  2. Biometrics

    3 publications · 64 citations

    3

    h-index

  3. Bioinformatics

    3 publications · 308 citations

    3

    h-index

  4. 2

    h-index

  5. Fisheries Research

    2 publications · 85 citations

    2

    h-index

1–5