Bayesian Generalized Linear Models

Bayesian Generalized Linear Model is a statistical modeling framework that extends generalized linear models by incorporating Bayesian inference, wherein prior distributions are assigned to model parameters and posterior distributions are derived through Bayes' theorem to account for parameter uncertainty in the analysis of non-normal response data.

132

Publications

8K

Citations

298

Authors

187

Institutions

Publications per year

2017–2026

36

Authors

298

  1. DG
    Daniel Gianola

    University of Wisconsin–Madison, United States

    4 publications · 117 citations

    4

    h-index

  2. DS
    Dirceu S. Reis

    Fundação Cearense de Meteorologia e Recursos Hídricos, Brazil

    4 publications · 188 citations

    4

    h-index

  3. JR
    Jery R. Stedinger

    Cornell University, United States

    4 publications · 188 citations

    4

    h-index

  4. JN
    J. N. Srivastava

    Colorado State University, United States

    3 publications · 687 citations

    3

    h-index

  5. JW
    Jacob Westfall

    The University of Texas at Austin, United States

    3 publications · 155 citations

    3

    h-index

1–5 of 298

Venues

  1. 7

    h-index

  2. Biometrics

    6 publications · 401 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 96 citations

    5

    h-index

  4. Biometrika

    5 publications · 803 citations

    5

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  5. Technometrics

    5 publications · 514 citations

    5

    h-index

1–5