Bayesian Generalized Additive Models

Bayesian Generalized Additive Models is a statistical modeling framework that extends generalized additive models by incorporating Bayesian inference to estimate smooth functional relationships between predictors and a response variable while accounting for uncertainty through prior distributions.

51

Publications

2.9K

Citations

181

Authors

126

Institutions

Publications per year

2017–2026

26

Authors

181

  1. NK
    Nadja Klein

    University of Göttingen, Germany

    6 publications · 407 citations

    6

    h-index

  2. TK
    Thomas Kneib

    University of Göttingen, Germany

    5 publications · 331 citations

    5

    h-index

  3. SL
    Stefan Lang

    University of Salzburg, Austria

    5 publications · 331 citations

    5

    h-index

  4. NU
    Nikolaus Umlauf

    Universität Innsbruck, Austria

    3 publications · 294 citations

    3

    h-index

  5. AZ
    Achim Zeileis

    Universität Innsbruck, Austria

    3 publications · 294 citations

    3

    h-index

1–5 of 181

Venues

  1. Journal of Statistical Software

    JSS · 4 publications · 322 citations

    4

    h-index

  2. PLoS ONE

    2 publications · 68 citations

    2

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 2 publications · 72 citations

    2

    h-index

  4. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 170 citations

    2

    h-index

  5. 2

    h-index

1–5