Bayesian Modeling

Bayesian Modeling is a statistical modeling framework that characterizes uncertainty in parameters through probability distributions by updating prior beliefs with observed data via Bayes' theorem.

14.4K

Publications

1.1M

Citations

26.2K

Authors

5.4K

Institutions

Publications per year

2017–2026

3.9K

Authors

26.2K

  1. AG
    Andrew Gelman

    Columbia University, United States

    52 publications · 30.1K citations

    36

    h-index

  2. JO
    James O. Berger

    Purdue University West Lafayette, United States

    46 publications · 12.2K citations

    32

    h-index

  3. AF
    A. F. M. Smith

    Imperial College London, United Kingdom

    40 publications · 18.1K citations

    31

    h-index

  4. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    51 publications · 7.1K citations

    31

    h-index

  5. AE
    Adrian E. Raftery

    University of Washington, United States

    38 publications · 18.1K citations

    30

    h-index

1–5 of 26.2K

Venues

  1. Journal of the American Statistical Association

    446 publications · 99.1K citations

    141

    h-index

  2. Water Resources Research

    188 publications · 20.4K citations

    77

    h-index

  3. arXiv (Cornell University)

    Repository · 436 publications · 38K citations

    70

    h-index

  4. 69

    h-index

  5. Reliability Engineering & System Safety

    207 publications · 14.9K citations

    66

    h-index

1–5