Bayesian Distribution Modeling

Bayesian Distribution Modeling is a probabilistic framework that integrates prior knowledge with observed data through Bayes' theorem to infer posterior distributions over model parameters, distinguishing itself from frequentist approaches by treating parameters as random variables and emphasizing uncertainty quantification in statistical inference.

40

Publications

3.6K

Citations

109

Authors

63

Institutions

Publications per year

2017–2026

20

Authors

109

  1. MB
    Munish Bhatia

    Lovely Professional University, India

    2 publications · 25 citations

    2

    h-index

  2. RS
    Robert Stewart

    University of Edinburgh, United Kingdom

    1 publications · 33 citations

    1

    h-index

  3. XG
    Xunhua Guo

    Tsinghua University, China

    1 publications · 20 citations

    1

    h-index

  4. SC
    Simone Ciuti

    University College Dublin, Ireland

    1 publications · 40 citations

    1

    h-index

  5. AE
    Andrew E. Schulman

    Environmental Protection Agency, United States

    1 publications · 25 citations

    1

    h-index

1–5 of 109

Venues

  1. Ecography

    3 publications · 156 citations

    3

    h-index

  2. 2

    h-index

  3. Journal of the American Statistical Association

    2 publications · 2.2K citations

    2

    h-index

  4. PeerJ

    1 publications · 17 citations

    1

    h-index

  5. Marine Technology and SNAME News

    1 publications · 33 citations

    1

    h-index

1–5