Approximate Bayesian Model Averaging

Approximate Bayesian Model Averaging is a computational method in Bayesian statistics that approximates the full Bayesian model averaging process by simplifying the model space exploration or posterior probability calculations to make inference more tractable under limited computational resources.

17

Publications

1.1K

Citations

52

Authors

41

Institutions

Publications per year

2017–2026

7

Authors

52

  1. SP
    Shawn P. Gilroy

    Louisiana State University, United States

    1 publications · 46 citations

    1

    h-index

  2. MA
    Marcela Andrea Espina de Franco

    Universidade Federal do Rio Grande do Sul, Brazil

    1 publications · 25 citations

    1

    h-index

  3. FS
    Filipe Santos

    Centre de Biologie pour la Gestion des Populations, France

    1 publications · 14 citations

    1

    h-index

  4. EK
    Eman Khorsheed

    1 publications · 44 citations

    1

    h-index

  5. CT
    Christopher T. Franck

    Virginia Tech, United States

    1 publications · 46 citations

    1

    h-index

1–5 of 52

Venues

  1. Statistics and Computing

    2 publications · 525 citations

    2

    h-index

  2. Journal of Hydrology

    1 publications · 48 citations

    1

    h-index

  3. Journal of Dairy Science

    JDS · 1 publications · 12 citations

    1

    h-index

  4. Multivariate Behavioral Research

    MBR · 1 publications · 28 citations

    1

    h-index

  5. Applied Mathematics & Information Sciences

    1 publications · 44 citations

    1

    h-index

1–5