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
Parents
13
1–5 of 13
26
4
h-index
2
h-index
1
h-index
NSF National Center for Atmospheric Research, United States
1 publications · 29 citations
1
h-index
1
h-index
1–5 of 26
22
Louisiana State University
Baton Rouge, United States
4 h-index
7 publications
373 citations
UCLA Health
Los Angeles, United States
1 h-index
1 publications
22 citations
London School of Hygiene & Tropical Medicine
London, United Kingdom
1 h-index
1 publications
21 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
1 h-index
1 publications
29 citations
University of Tabriz
Tabriz, Iran
1 h-index
1 publications
89 citations
China Academy of Engineering Physics
Mianyang, China
1 h-index
1 publications
32 citations
Ningbo University of Technology
Ningbo, China
1 h-index
2 publications
64 citations
University of Pittsburgh
Pittsburgh, United States
1 h-index
1 publications
12 citations
1–8 of 22
2 publications · 55 citations
2
h-index
1 publications · 58 citations
1
h-index
1 publications · 52 citations
1
h-index
Repository · 1 publications · 16 citations
1
h-index
1 publications · 48 citations
1
h-index
1–5