Approximate Bayesian Computation

Approximate Bayesian Computation is a class of computational methods in Bayesian inference that approximates the posterior distribution by bypassing the evaluation of the likelihood function through the use of summary statistics and simulation-based comparison between observed and synthetic data.

591

Publications

52K

Citations

1.5K

Authors

752

Institutions

Publications per year

2017–2026

248

Authors

1.5K

  1. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    24 publications · 4K citations

    19

    h-index

  2. MB
    Mark Beaumont

    University of Reading, United Kingdom

    18 publications · 7.4K citations

    18

    h-index

  3. AE
    Arnaud Estoup

    Centre de Biologie pour la Gestion des Populations, France

    16 publications · 4.3K citations

    16

    h-index

  4. MP
    Michael P. H. Stumpf

    Imperial College London, United Kingdom

    15 publications · 2.9K citations

    15

    h-index

  5. CD
    Christopher Drovandi

    Queensland University of Technology, Australia

    16 publications · 808 citations

    15

    h-index

1–5 of 1.5K

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 8.3K citations

    23

    h-index

  2. Genetics

    16 publications · 5.2K citations

    16

    h-index

  3. Molecular Ecology Resources

    17 publications · 1.4K citations

    14

    h-index

  4. Molecular Ecology

    14 publications · 1.5K citations

    14

    h-index

  5. Statistics and Computing

    13 publications · 2.3K citations

    13

    h-index

1–5