Bayesian Computation

Bayesian Computation is a branch of statistical computing that focuses on the development and application of computational methods for performing Bayesian inference, distinguished by its reliance on posterior distribution approximation through techniques such as Markov chain Monte Carlo and variational inference.

4.3K

Publications

354.4K

Citations

8.3K

Authors

2.3K

Institutions

Publications per year

2017–2026

1.1K

Authors

8.3K

  1. DB
    David B. Dunson

    Duke University, United States

    47 publications · 3.7K citations

    32

    h-index

  2. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    31 publications · 4.3K citations

    23

    h-index

  3. AG
    Andrew Gelman

    Columbia University, United States

    20 publications · 3.6K citations

    18

    h-index

  4. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    22 publications · 6.6K citations

    18

    h-index

  5. MW
    Max Welling

    University of Amsterdam, Netherlands

    19 publications · 1.1K citations

    17

    h-index

1–5 of 8.3K

Venues

  1. Journal of the American Statistical Association

    119 publications · 31K citations

    60

    h-index

  2. arXiv (Cornell University)

    Repository · 353 publications · 16.1K citations

    58

    h-index

  3. Biometrika

    72 publications · 13.4K citations

    40

    h-index

  4. 39

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 70 publications · 4.7K citations

    33

    h-index

1–5