Graphical Models
Graphical Models is a statistical framework that represents complex probabilistic relationships among variables through a graph structure, where nodes denote random variables and edges encode conditional dependencies, thereby distinguishing it from other statistical models by its explicit use of graph theory to factorize joint probability distributions.
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2017–2026
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Robert L. Camp, Marisa Dolled‐Filhart, David L. Rimm · Clinical Cancer Research · 2004 · 3.9K citations
Medicine, Biostatistics, Oncology +17
An Introduction to Variational Methods for Graphical Models
Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola et al. · Machine Learning · 1999 · 3.8K citations · Full text
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Carnegie Mellon University
Pittsburgh, United States
50 h-index
242 publications
38.2K citations
University of Washington
Seattle, United States
48 h-index
218 publications
28.3K citations
Stanford University
Stanford, United States
45 h-index
145 publications
18.8K citations
University of California, Berkeley
Berkeley, United States
42 h-index
154 publications
13.2K citations
Massachusetts Institute of Technology
Cambridge, United States
35 h-index
156 publications
11.8K citations
ETH Zurich
Zurich, Switzerland
30 h-index
83 publications
9.6K citations
University of California, Los Angeles
Los Angeles, United States
29 h-index
86 publications
23.3K citations
University of Michigan
Ann Arbor, United States
29 h-index
99 publications
6K citations
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Repository · 313 publications · 17.8K citations
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