Conditional Probability Models
Conditional Probability Models is a probabilistic modeling framework that specifies the distribution of a target variable given the values of other variables, distinguishing itself by formalizing dependencies through conditional distributions rather than joint or marginal distributions.
2.2K
Publications
209.7K
Citations
5.6K
Authors
1.8K
Institutions
Publications per year
2017–2026
486
Variants
Conditional Probabilistic Models
Parents
2.2K
1–5 of 2.2K
5.6K
11
h-index
9
h-index
8
h-index
8
h-index
7
h-index
1–5 of 5.6K
1.8K
Stanford University
Stanford, United States
23 h-index
48 publications
5.3K citations
Carnegie Mellon University
Pittsburgh, United States
21 h-index
52 publications
2.6K citations
University of California, Berkeley
Berkeley, United States
20 h-index
40 publications
5.4K citations

Cornell University
Ithaca, United States
17 h-index
31 publications
1.3K citations
Harvard University
Cambridge, United States
17 h-index
42 publications
2.3K citations
Tsinghua University
Beijing, China
16 h-index
58 publications
5.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
16 h-index
42 publications
2.1K citations
University of Washington
Seattle, United States
16 h-index
40 publications
2.2K citations
1–8 of 1.8K
32 publications · 14.8K citations
25
h-index
Repository · 55 publications · 5.9K citations
23
h-index
17 publications · 986 citations
15
h-index
14 publications · 950 citations
13
h-index
12 publications · 771 citations
12
h-index
1–5