Markov Network Models
Markov Network Models is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via an undirected graph, distinguished from other graphical models by its use of undirected edges to encode symmetric dependence relations and its foundation in the Markov property for characterizing local independence.
309
Publications
23.2K
Citations
756
Authors
362
Institutions
Publications per year
2017–2026
61
Variants
MrfUndirected Graphical Models
Parents
309
William T. Freeman · International Journal of Computer Vision · 2000 · 1.5K citations
Reversible Markov Chains and Random Walks on Graphs
David Aldous, James Allen Fill · 2014 · 968 citations
Generalized Belief Propagation
Jonathan S. Yedidia, William T. Freeman, Yair Weiss · 2000 · 916 citations
1–5 of 309
756
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362
Carnegie Mellon University
Pittsburgh, United States
12 h-index
26 publications
2.9K citations
University of Washington
Seattle, United States
11 h-index
21 publications
1.5K citations
Stanford University
Stanford, United States
10 h-index
26 publications
5.2K citations
The University of Texas at Austin
Austin, United States
7 h-index
17 publications
1.2K citations
University of California, Berkeley
Berkeley, United States
6 h-index
8 publications
1.1K citations
Northwestern University
Evanston, United States
6 h-index
14 publications
1.1K citations

Beihang University
Beijing, China
5 h-index
11 publications
588 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
14 publications
466 citations
1–8 of 362
Repository · 19 publications · 1K citations
15
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TPAMI · 7 publications · 1.7K citations
7
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3 publications · 205 citations
3
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Repository · 3 publications · 103 citations
3
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3 publications · 161 citations
3
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