Bayesian Hierarchical Forecasting

Bayesian Hierarchical Forecasting is a hierarchical forecasting framework that uses Bayesian inference to model uncertainty and information sharing across multiple levels of a hierarchy, distinguishing itself by formally incorporating prior knowledge and probabilistic dependencies to improve forecast coherence and accuracy.

77

Publications

4.6K

Citations

272

Authors

194

Institutions

Publications per year

2017–2026

28

Authors

272

  1. CK
    Christopher K. Wikle

    University of Missouri, United States

    4 publications · 263 citations

    4

    h-index

  2. LM
    L. Mark Berliner

    The Ohio State University, United States

    4 publications · 280 citations

    4

    h-index

  3. WA
    Wagner A. Kamakura

    Rice University, United States

    2 publications · 103 citations

    2

    h-index

  4. PB
    Peter Boatwright

    Carnegie Mellon University, United States

    2 publications · 103 citations

    2

    h-index

  5. SK
    Sujit K. Sahu

    University of Southampton, United Kingdom

    2 publications · 39 citations

    2

    h-index

1–5 of 272

Venues

  1. Water Resources Research

    4 publications · 78 citations

    4

    h-index

  2. 3

    h-index

  3. Ecological Applications

    3 publications · 84 citations

    3

    h-index

  4. PLoS ONE

    3 publications · 145 citations

    3

    h-index

  5. Journal of Forecasting

    3 publications · 53 citations

    3

    h-index

1–5