Posterior Distribution Approximation

Posterior Distribution Approximation is a computational method in Bayesian statistics that approximates the posterior distribution of model parameters by constructing a tractable probability distribution closest to the true posterior under a specified divergence measure.

1.5K

Publications

148.5K

Citations

3K

Authors

1.1K

Institutions

Publications per year

2017–2026

453

Authors

3K

  1. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    15 publications · 2K citations

    14

    h-index

  2. DB
    David B. Dunson

    Duke University, United States

    12 publications · 639 citations

    12

    h-index

  3. AV
    Aki Vehtari

    Aalto University, Finland

    12 publications · 446 citations

    12

    h-index

  4. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    11 publications · 2.8K citations

    11

    h-index

  5. JG
    John Geweke

    University of Technology Sydney, Australia

    10 publications · 1.1K citations

    10

    h-index

1–5 of 3K

Venues

  1. arXiv (Cornell University)

    Repository · 157 publications · 9.2K citations

    42

    h-index

  2. Journal of the American Statistical Association

    67 publications · 13.9K citations

    37

    h-index

  3. The Annals of Statistics

    34 publications · 3.1K citations

    25

    h-index

  4. Biometrika

    28 publications · 3.1K citations

    23

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 30 publications · 3K citations

    20

    h-index

1–5