Bayesian Model Selection

Bayesian Model Selection is a statistical framework within Bayesian inference that uses posterior model probabilities, derived from prior probabilities and marginal likelihoods, to compare and select among competing models based on their joint fit to observed data and modeling assumptions.

1.3K

Publications

206.5K

Citations

3.1K

Authors

1.4K

Institutions

Publications per year

2017–2026

358

Authors

3.1K

  1. KF
    Karl Friston

    University College London, United Kingdom

    18 publications · 4.4K citations

    17

    h-index

  2. HH
    Herbert Hoijtink

    Utrecht University, Netherlands

    16 publications · 969 citations

    15

    h-index

  3. AE
    Adrian E. Raftery

    University of Washington, United States

    13 publications · 14.2K citations

    13

    h-index

  4. JM
    Joris Mulder

    Tilburg University, Netherlands

    11 publications · 591 citations

    11

    h-index

  5. IK
    Irene Klugkist

    Utrecht University, Netherlands

    10 publications · 683 citations

    10

    h-index

1–5 of 3.1K

Venues

  1. Journal of the American Statistical Association

    43 publications · 10.1K citations

    33

    h-index

  2. Monthly Notices of the Royal Astronomical Society

    28 publications · 1.8K citations

    19

    h-index

  3. Physical review. D/Physical review. D.

    PRD · 21 publications · 1.2K citations

    19

    h-index

  4. NeuroImage

    18 publications · 3.5K citations

    17

    h-index

  5. Bayesian Analysis

    19 publications · 977 citations

    17

    h-index

1–5