Importance Sampling Approximate Bayesian Computation

Importance Sampling Approximate Bayesian Computation is a methodological framework within approximate Bayesian computation that employs importance sampling to weight candidate parameter values according to their proximity to observed data, thereby enhancing computational efficiency in approximating posterior distributions.

28

Publications

1.9K

Citations

73

Authors

56

Institutions

Publications per year

2017–2026

11

Authors

73

  1. JM
    Jean‐Michel Marin

    Centre de Recherche en Mathématiques de la Décision, France

    2 publications · 746 citations

    2

    h-index

  2. OP
    Omiros Papaspiliopoulos

    Universitat Pompeu Fabra, Spain

    2 publications · 146 citations

    2

    h-index

  3. DS
    Daniel Sanz-Alonso

    University of Chicago, United States

    2 publications · 146 citations

    2

    h-index

  4. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    2 publications · 746 citations

    2

    h-index

  5. SA
    Sergios Agapiou

    University of Warwick, United Kingdom

    2 publications · 146 citations

    2

    h-index

1–5 of 73

Venues

  1. Computational Statistics & Data Analysis

    CSDA · 2 publications · 60 citations

    2

    h-index

  2. Biometrika

    2 publications · 656 citations

    2

    h-index

  3. Atmospheric Environment

    1 publications · 17 citations

    1

    h-index

  4. The Astrophysical Journal

    ApJ · 1 publications · 51 citations

    1

    h-index

  5. Statistical Science

    1 publications · 136 citations

    1

    h-index

1–5