Bayesian Dirichlet-multinomial Models
Bayesian Dirichlet-Multinomial Models is a hierarchical Bayesian model that combines the multinomial likelihood of categorical count data with a Dirichlet prior distribution to enable probabilistic inference on category probabilities while accounting for overdispersion and uncertainty in parameter estimation.
52
Publications
3.3K
Citations
138
Authors
96
Institutions
Publications per year
2017–2026
18
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Bayesian correlated clustering to integrate multiple datasets
Paul Kirk, Jim E. Griffin, Richard S. Savage et al. · Bioinformatics · 2012 · 245 citations · Full text
Audun Jøsang, Jochen Haller · 2007 · 217 citations
Natural Sciences, Information Systems, Bayesian Data Analysis +13
1–5 of 52
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The University of Texas MD Anderson Cancer Center, United States
2 publications · 154 citations
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The University of Texas Health Science Center at Houston, United States
2 publications · 33 citations
2
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1 publications · 24 citations
1
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1–5 of 138
96
The University of Texas MD Anderson Cancer Center
Houston, United States
3 h-index
8 publications
642 citations
NOAA National Marine Fisheries Service
Silver Spring, United States
3 h-index
7 publications
361 citations
Texas A&M University
College Station, United States
2 h-index
4 publications
124 citations
University College London
London, United Kingdom
2 h-index
3 publications
214 citations

University of Warwick
Coventry, United Kingdom
2 h-index
6 publications
1.2K citations

Rice University
Houston, United States
2 h-index
4 publications
210 citations
University of Florida
Gainesville, United States
2 h-index
3 publications
190 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
2 publications
283 citations
1–8 of 96
4 publications · 205 citations
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3 publications · 64 citations
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3 publications · 308 citations
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2 publications · 139 citations
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2 publications · 85 citations
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