Bayesian Marginal Likelihood

Bayesian Marginal Likelihood is the probability of observed data under a given model, obtained by integrating the likelihood function over the prior distribution of the model parameters, thereby serving as a normalizing constant in Bayesian posterior computation.

76

Publications

7.3K

Citations

194

Authors

112

Institutions

Publications per year

2017–2026

22

Authors

194

  1. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    4 publications · 405 citations

    4

    h-index

  2. HS
    Henrik Singmann

    University of Zurich, Switzerland

    3 publications · 390 citations

    3

    h-index

  3. QF
    Quentin F. Gronau

    University of Amsterdam, Netherlands

    3 publications · 390 citations

    3

    h-index

  4. GB
    Guy Baele

    KU Leuven, Belgium

    3 publications · 1.4K citations

    3

    h-index

  5. PL
    Philippe Lemey

    KU Leuven, Belgium

    3 publications · 1.4K citations

    3

    h-index

1–5 of 194

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 221 citations

    9

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 2 publications · 179 citations

    2

    h-index

  3. Journal of the American Statistical Association

    2 publications · 1.1K citations

    2

    h-index

  4. Bayesian Analysis

    2 publications · 92 citations

    2

    h-index

  5. Systematic Biology

    2 publications · 1.4K citations

    2

    h-index

1–5