Bayesian Multilevel Inference

Bayesian Multilevel Inference is a statistical inference framework that belongs to the class of hierarchical modeling methods, distinguished by its incorporation of prior distributions at multiple levels of a parameter hierarchy to enable coherent uncertainty quantification and information sharing across groups.

326

Publications

30K

Citations

1.3K

Authors

554

Institutions

Publications per year

2017–2026

206

Authors

1.3K

  1. PB
    Paul‐Christian Bürkner

    University of Stuttgart, Germany

    8 publications · 10K citations

    9

    h-index

  2. HM
    Humberto M. Carvalho

    University of Coimbra, Portugal

    8 publications · 229 citations

    8

    h-index

  3. WJ
    William J. Browne

    University of Bristol, United Kingdom

    6 publications · 1.4K citations

    6

    h-index

  4. AG
    Andrew Gelman

    Columbia University, United States

    6 publications · 691 citations

    6

    h-index

  5. CS
    Csaba Szalai

    Semmelweis University, Hungary

    6 publications · 258 citations

    6

    h-index

1–5 of 1.3K

Venues

  1. PLoS ONE

    11 publications · 355 citations

    11

    h-index

  2. PubMed

    Repository · 6 publications · 147 citations

    6

    h-index

  3. Frontiers in Psychology

    4 publications · 91 citations

    5

    h-index

  4. Structural Equation Modeling A Multidisciplinary Journal

    SEM · 5 publications · 252 citations

    5

    h-index

  5. 5

    h-index

1–5