Bayesian Multilevel Modeling

Bayesian Multilevel Modeling is a statistical modeling framework that belongs to the class of hierarchical models and is distinguished by its incorporation of prior distributions on parameters at multiple levels of data structure, enabling coherent uncertainty quantification and partial pooling through Bayesian inference.

385

Publications

31.5K

Citations

1.1K

Authors

505

Institutions

Publications per year

2017–2026

143

Authors

1.1K

  1. TS
    Tarek Sayed

    University of British Columbia, Canada

    8 publications · 542 citations

    8

    h-index

  2. BP
    Bradley P. Carlin

    University of Minnesota, United States

    7 publications · 330 citations

    7

    h-index

  3. PB
    Paul‐Christian Bürkner

    University of Stuttgart, Germany

    7 publications · 10.2K citations

    7

    h-index

  4. MA
    Mohamed Abdel‐Aty

    University of Central Florida, United States

    5 publications · 766 citations

    5

    h-index

  5. HH
    Helai Huang

    Central South University, China

    5 publications · 1.1K citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Journal of the American Statistical Association

    18 publications · 1.1K citations

    16

    h-index

  2. Accident Analysis & Prevention

    16 publications · 1.7K citations

    15

    h-index

  3. Statistics in Medicine

    17 publications · 464 citations

    13

    h-index

  4. Biometrics

    13 publications · 646 citations

    12

    h-index

  5. 9

    h-index

1–5