Adaptive Approximate Bayesian Computation

Adaptive Approximate Bayesian Computation is a methodological extension of Approximate Bayesian Computation that dynamically adjusts algorithmic components—such as tolerance levels, proposal distributions, or kernel weights—during the iterative sampling process to improve computational efficiency and posterior approximation accuracy.

31

Publications

2.8K

Citations

101

Authors

67

Institutions

Publications per year

2017–2026

18

Authors

101

  1. CB
    C. Barnes

    University College London, United Kingdom

    2 publications · 348 citations

    2

    h-index

  2. JL
    Jan-Matthis Lueckmann

    Center of Advanced European Studies and Research, Germany

    2 publications · 162 citations

    2

    h-index

  3. GB
    Giacomo Bassetto

    Center of Advanced European Studies and Research, Germany

    2 publications · 162 citations

    2

    h-index

  4. CD
    Christopher Drovandi

    Queensland University of Technology, Australia

    2 publications · 342 citations

    2

    h-index

  5. JH
    Jakob H. Macke

    Max Planck Society, Germany

    2 publications · 162 citations

    2

    h-index

1–5 of 101

Venues

  1. The Journal of Chemical Physics

    1 publications · 21 citations

    1

    h-index

  2. Environmental Modelling & Software

    1 publications · 33 citations

    1

    h-index

  3. Cognitive Science

    1 publications · 53 citations

    1

    h-index

  4. Nature Computational Science

    1 publications · 36 citations

    1

    h-index

  5. Nature Protocols

    1 publications · 249 citations

    1

    h-index

1–5