Rejection Approximate Bayesian Computation

Rejection Approximate Bayesian Computation is a likelihood-free inference method that belongs to the class of Approximate Bayesian Computation techniques, distinguished by its use of direct rejection sampling to accept parameter proposals only when simulated data fall within a predefined tolerance threshold of the observed data.

23

Publications

1.9K

Citations

71

Authors

49

Institutions

Publications per year

2017–2026

8

Authors

71

  1. MP
    Michael P. H. Stumpf

    Imperial College London, United Kingdom

    3 publications · 414 citations

    3

    h-index

  2. SA
    Scott A. Sisson

    UNSW Sydney, Australia

    2 publications · 90 citations

    2

    h-index

  3. PK
    Paul Kirk

    Imperial College London, United Kingdom

    2 publications · 392 citations

    2

    h-index

  4. CB
    C. Barnes

    University College London, United Kingdom

    2 publications · 392 citations

    2

    h-index

  5. GW
    Gareth W. Peters

    University College London, United Kingdom

    2 publications · 90 citations

    2

    h-index

1–5 of 71

Venues

  1. Bioinformatics

    3 publications · 196 citations

    3

    h-index

  2. PLoS ONE

    2 publications · 98 citations

    2

    h-index

  3. Statistical Applications in Genetics and Molecular Biology

    SAGMB · 2 publications · 81 citations

    2

    h-index

  4. Genetics

    1 publications · 273 citations

    1

    h-index

  5. Geoscientific model development

    1 publications · 23 citations

    1

    h-index

1–5