Bayesian Predictive Synthesis

Bayesian Predictive Synthesis is a Bayesian framework for combining predictive distributions from multiple models or experts by explicitly modeling their dependencies and assigning dynamic, context-dependent weights through a hierarchical prior structure.

16

Publications

3K

Citations

41

Authors

27

Institutions

Publications per year

2017–2026

6

Authors

41

  1. MW
    Mike West

    Duke University, United States

    3 publications · 196 citations

    3

    h-index

  2. WF
    William F. Eddy

    Carnegie Mellon University, United States

    2 publications · 1.1K citations

    2

    h-index

  3. DW
    Derek W. Bunn

    London Business School, United Kingdom

    2 publications · 38 citations

    2

    h-index

  4. KM
    Kenichiro McAlinn

    Temple University, United States

    2 publications · 177 citations

    2

    h-index

  5. SG
    Seymour Geisser

    University of Minnesota, United States

    2 publications · 1.1K citations

    2

    h-index

1–5 of 41

Venues

  1. Journal of the American Statistical Association

    3 publications · 1.2K citations

    3

    h-index

  2. Ecological Modelling

    2 publications · 543 citations

    2

    h-index

  3. Journal of Marketing Research

    2 publications · 38 citations

    2

    h-index

  4. Journal of Econometrics

    1 publications · 116 citations

    1

    h-index

  5. Psychological Bulletin

    1 publications · 36 citations

    1

    h-index

1–5