Bayesian Inference

Bayesian Inference is a statistical inference method that updates the probability for a hypothesis as evidence accumulates, by applying Bayes' theorem with a prior distribution to obtain a posterior distribution that reflects refined beliefs about model parameters.

36.4K

Publications

3M

Citations

56.2K

Authors

8.3K

Institutions

Publications per year

2017–2026

8.6K

Authors

56.2K

  1. DB
    David B. Dunson

    Duke University, United States

    141 publications · 9.7K citations

    57

    h-index

  2. KF
    Karl Friston

    University College London, United Kingdom

    83 publications · 18.5K citations

    55

    h-index

  3. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    117 publications · 16.9K citations

    55

    h-index

  4. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    92 publications · 17K citations

    50

    h-index

  5. JO
    James O. Berger

    Purdue University West Lafayette, United States

    89 publications · 17.1K citations

    49

    h-index

1–5 of 56.2K

Venues

  1. Journal of the American Statistical Association

    1.1K publications · 219.7K citations

    201

    h-index

  2. 126

    h-index

  3. arXiv (Cornell University)

    Repository · 1.5K publications · 85.5K citations

    117

    h-index

  4. The Annals of Statistics

    530 publications · 73.4K citations

    117

    h-index

  5. Biometrika

    502 publications · 73.5K citations

    112

    h-index

1–5