Approximate Bayesian Inference

Approximate Bayesian Inference is a methodological framework within Bayesian statistics that approximates the posterior distribution of model parameters when exact inference is computationally intractable, distinguishing itself from exact Bayesian methods by trading full probabilistic precision for scalability and efficiency.

870

Publications

122.9K

Citations

1.9K

Authors

808

Institutions

Publications per year

2017–2026

298

Authors

1.9K

  1. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    24 publications · 3.2K citations

    19

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    21 publications · 9K citations

    19

    h-index

  3. DP
    Dennis Prangle

    Newcastle University, United Kingdom

    12 publications · 1.1K citations

    12

    h-index

  4. KF
    Karl Friston

    University College London, United Kingdom

    12 publications · 2.3K citations

    12

    h-index

  5. JM
    Jean‐Michel Marin

    Centre de Recherche en Mathématiques de la Décision, France

    11 publications · 2.6K citations

    11

    h-index

1–5 of 1.9K

Venues

  1. arXiv (Cornell University)

    Repository · 131 publications · 20.2K citations

    45

    h-index

  2. Statistics and Computing

    17 publications · 2.8K citations

    17

    h-index

  3. Bayesian Analysis

    15 publications · 870 citations

    14

    h-index

  4. Biometrika

    14 publications · 1.8K citations

    14

    h-index

  5. Bioinformatics

    14 publications · 2.7K citations

    13

    h-index

1–5