Hierarchical Model Averaging

Hierarchical Model Averaging is a Bayesian model averaging framework that integrates multiple levels of model uncertainty by structuring candidate models into a hierarchical prior distribution, thereby differentiating itself from flat model averaging approaches through the explicit representation of nested or grouped model structures.

13

Publications

487

Citations

26

Authors

22

Institutions

Publications per year

2017–2026

3

Authors

26

  1. FT
    Frank T.‐C. Tsai

    Louisiana State University, United States

    4 publications · 211 citations

    4

    h-index

  2. NC
    Nima Chitsazan

    Louisiana State University, United States

    2 publications · 114 citations

    2

    h-index

  3. XG
    Xuefei Guan

    Beihang University, China

    1 publications · 32 citations

    1

    h-index

  4. DH
    Dorit Hammerling

    NSF National Center for Atmospheric Research, United States

    1 publications · 29 citations

    1

    h-index

  5. DP
    David P. Farrington

    University of Cambridge, United Kingdom

    1 publications · 12 citations

    1

    h-index

1–5 of 26

Venues

  1. Statistics in Medicine

    2 publications · 55 citations

    2

    h-index

  2. Bulletin of the Psychonomic Society

    1 publications · 58 citations

    1

    h-index

  3. Psychology of Addictive Behaviors

    1 publications · 52 citations

    1

    h-index

  4. EUR Research Repository (Erasmus University Rotterdam)

    Repository · 1 publications · 16 citations

    1

    h-index

  5. Water Resources Research

    1 publications · 48 citations

    1

    h-index

1–5