Bayesian Linear Mixed Models

Bayesian Linear Mixed Models is a statistical modeling framework within the class of linear mixed models that incorporates Bayesian inference to estimate fixed and random effects by specifying prior distributions on model parameters and updating them to posterior distributions through the application of Bayes' theorem.

138

Publications

9.4K

Citations

717

Authors

380

Institutions

Publications per year

2017–2026

74

Variants

Bayesian Hierarchical Linear ModelsBayesian Multilevel Models

Authors

717

  1. SV
    Shravan Vasishth

    University of Potsdam, Germany

    5 publications · 509 citations

    5

    h-index

  2. BN
    Bruno Nicenboim

    University of Potsdam, Germany

    3 publications · 303 citations

    3

    h-index

  3. DI
    David I. Stuart

    University of Oxford, United Kingdom

    2 publications · 161 citations

    2

    h-index

  4. SH
    Sarah Hoosdally

    University of Oxford, United Kingdom

    2 publications · 161 citations

    2

    h-index

  5. VH
    Víctor H. Lachos

    Universidade Estadual de Campinas (UNICAMP), Brazil

    2 publications · 115 citations

    2

    h-index

1–5 of 717

Venues

  1. Computational Statistics & Data Analysis

    CSDA · 7 publications · 317 citations

    7

    h-index

  2. Journal of Statistical Planning and Inference

    6 publications · 299 citations

    6

    h-index

  3. PLoS ONE

    5 publications · 111 citations

    5

    h-index

  4. Scientific Reports

    5 publications · 223 citations

    5

    h-index

  5. Genetics Selection Evolution

    GSE · 3 publications · 268 citations

    3

    h-index

1–5