Bayesian Log-linear Models

Bayesian Log-Linear Models is a statistical modeling framework that extends classical log-linear models by incorporating Bayesian inference, wherein prior distributions are assigned to model parameters and posterior distributions are derived to estimate associations in contingency table data while accounting for uncertainty.

46

Publications

2.1K

Citations

148

Authors

99

Institutions

Publications per year

2017–2026

13

Authors

148

  1. TP
    Taesung Park

    Seoul National University, South Korea

    4 publications · 132 citations

    4

    h-index

  2. TL
    Tom Leonard

    University of Wisconsin–Madison, United States

    4 publications · 132 citations

    4

    h-index

  3. JH
    James H. Albert

    Bowling Green State University, United States

    3 publications · 102 citations

    3

    h-index

  4. AS
    Adam S. Vaughan

    Centers for Disease Control and Prevention, United States

    3 publications · 109 citations

    3

    h-index

  5. JH
    J.S. Hsu

    Oak Ridge National Laboratory, United States

    2 publications · 84 citations

    2

    h-index

1–5 of 148

Venues

  1. 7

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  2. Biometrika

    2 publications · 70 citations

    2

    h-index

  3. 2

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  4. PLoS ONE

    2 publications · 83 citations

    2

    h-index

  5. Biometrics

    2 publications · 34 citations

    2

    h-index

1–5