Probabilistic Mixture Modeling
Probabilistic Mixture Modeling is a statistical modeling framework that characterizes the combined effects of multiple agents in a mixture by representing dose-response relationships through probability distributions to quantify uncertainty and variability in component interactions.
148
Publications
12.1K
Citations
460
Authors
251
Institutions
Publications per year
2017–2026
39
148
1–5 of 148
460
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251
University of California, Irvine
Irvine, United States
5 h-index
12 publications
562 citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
11 publications
1.4K citations
Harvard University
Cambridge, United States
4 h-index
7 publications
565 citations

University of Alberta
Edmonton, Canada
3 h-index
5 publications
146 citations

University of Birmingham
Birmingham, United Kingdom
3 h-index
3 publications
61 citations
University of California, Berkeley
Berkeley, United States
3 h-index
5 publications
2.9K citations
University of Iowa
Iowa City, United States
3 h-index
3 publications
228 citations
Microsoft (United States)
Redmond, United States
3 h-index
4 publications
327 citations
1–8 of 251
Repository · 6 publications · 174 citations
6
h-index
4 publications · 99 citations
4
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Repository · 4 publications · 129 citations
4
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3 publications · 370 citations
3
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FSI · 2 publications · 27 citations
2
h-index
1–5