Bayesian Model Building

Bayesian Model Building is the systematic process of constructing probabilistic models that incorporate prior knowledge and observed data within a Bayesian framework to infer posterior distributions over unknown parameters.

7.4K

Publications

503.3K

Citations

17.5K

Authors

4.1K

Institutions

Publications per year

2017–2026

2.3K

Authors

17.5K

  1. DB
    David B. Dunson

    Duke University, United States

    42 publications · 3.5K citations

    28

    h-index

  2. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    30 publications · 6.4K citations

    23

    h-index

  3. NF
    Norman Fenton

    Queen Mary University of London, United Kingdom

    29 publications · 2.2K citations

    21

    h-index

  4. AG
    Andrew Gelman

    Columbia University, United States

    21 publications · 13.2K citations

    19

    h-index

  5. MN
    Martin Neil

    Queen Mary University of London, United Kingdom

    24 publications · 1.9K citations

    19

    h-index

1–5 of 17.5K

Venues

  1. Journal of the American Statistical Association

    145 publications · 21.2K citations

    60

    h-index

  2. Reliability Engineering & System Safety

    138 publications · 10K citations

    54

    h-index

  3. arXiv (Cornell University)

    Repository · 165 publications · 8.7K citations

    44

    h-index

  4. Biometrics

    95 publications · 6.1K citations

    40

    h-index

  5. Technometrics

    73 publications · 7.4K citations

    38

    h-index

1–5