Bayesian Multilevel Modeling
Bayesian Multilevel Modeling is a statistical modeling framework that belongs to the class of hierarchical models and is distinguished by its incorporation of prior distributions on parameters at multiple levels of data structure, enabling coherent uncertainty quantification and partial pooling through Bayesian inference.
385
Publications
31.5K
Citations
1.1K
Authors
505
Institutions
Publications per year
2017–2026
143
Variants
Bayesian Hierarchical Modeling
Parents
385
1–5 of 385
1.1K
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h-index
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505
University of Michigan
Ann Arbor, United States
12 h-index
23 publications
1.8K citations

University of British Columbia
Vancouver, Canada
11 h-index
20 publications
1.3K citations

Imperial College London
London, United Kingdom
10 h-index
18 publications
554 citations
Duke University
Durham, United States
10 h-index
21 publications
1.1K citations
Texas A&M University
College Station, United States
9 h-index
15 publications
763 citations
Columbia University
New York, United States
9 h-index
18 publications
2K citations

University of Minnesota
Minneapolis, United States
9 h-index
12 publications
505 citations
The Ohio State University
Columbus, United States
8 h-index
9 publications
451 citations
1–8 of 505
18 publications · 1.1K citations
16
h-index
16 publications · 1.7K citations
15
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17 publications · 464 citations
13
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13 publications · 646 citations
12
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9 publications · 361 citations
9
h-index
1–5