Marginal Likelihood Estimation

Marginal Likelihood Estimation is a statistical inference method that belongs to the class of likelihood-based evaluation techniques, specifically defined by its computation of the probability of observed data under a given model by integrating the likelihood function over the entire parameter space with respect to the prior distribution.

723

Publications

77.8K

Citations

1.3K

Authors

619

Institutions

Publications per year

2017–2026

194

Variants

Model Evidence Estimation

Authors

1.3K

  1. SC
    Siddhartha Chib

    Washington University in St. Louis, United States

    8 publications · 3.6K citations

    8

    h-index

  2. AG
    Andrew Gordon Wilson

    Cornell University, United States

    7 publications · 302 citations

    7

    h-index

  3. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    7 publications · 844 citations

    7

    h-index

  4. CE
    Carl Edward Rasmussen

    University of Cambridge, United Kingdom

    7 publications · 705 citations

    7

    h-index

  5. GP
    Gianluigi Pillonetto

    University of Padua, Italy

    7 publications · 427 citations

    7

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 80 publications · 3K citations

    28

    h-index

  2. Journal of the American Statistical Association

    37 publications · 12.1K citations

    25

    h-index

  3. Applied Psychological Measurement

    25 publications · 2.1K citations

    20

    h-index

  4. Biometrika

    18 publications · 3.7K citations

    18

    h-index

  5. Biometrics

    17 publications · 2.7K citations

    14

    h-index

1–5