Bayesian Network Structure Learning
Bayesian Network Structure Learning is a computational method in probabilistic graphical modeling that identifies the directed acyclic graph structure representing conditional dependencies among random variables from observational data.
764
Publications
57.3K
Citations
1.6K
Authors
648
Institutions
Publications per year
2017–2026
195
Variants
Bayesian Network Structure Discovery
Parents
764
Dynamic bayesian networks: representation, inference and learning
Kevin P. Murphy, Stuart Russell · 2002 · 2.6K citations
1–5 of 764
1.6K
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16
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Dalle Molle Institute for Artificial Intelligence Research, Switzerland
11 publications · 2.2K citations
11
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11
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Dalle Molle Institute for Artificial Intelligence Research, Switzerland
10 publications · 650 citations
10
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1–5 of 1.6K
648

University of Helsinki
Helsinki, Finland
14 h-index
34 publications
1.4K citations
Stanford University
Stanford, United States
14 h-index
32 publications
5.4K citations
Hebrew University of Jerusalem
Jerusalem, Israel
13 h-index
19 publications
2.7K citations
Microsoft (United States)
Redmond, United States
12 h-index
21 publications
14.6K citations
Helsinki Institute for Information Technology
Espoo, Finland
12 h-index
25 publications
1.4K citations
University of California, Los Angeles
Los Angeles, United States
12 h-index
29 publications
1.5K citations
Aalborg University
Aalborg, Denmark
11 h-index
18 publications
1K citations
Iowa State University
Ames, United States
11 h-index
19 publications
499 citations
1–8 of 648
Repository · 76 publications · 5.1K citations
32
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31 publications · 1.8K citations
23
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12 publications · 13.4K citations
12
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Conference · 11 publications · 296 citations
11
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12 publications · 426 citations
11
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