Bayesian Graphical Models
Bayesian Graphical Models is a probabilistic graphical model that represents conditional dependencies among random variables through a directed acyclic graph, where the joint probability distribution is specified using Bayesian inference principles to encode uncertainty and facilitate coherent updating of beliefs.
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Citations
2.7K
Authors
1K
Institutions
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2017–2026
262
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Stanford University
Stanford, United States
26 h-index
68 publications
12.8K citations
University of California, Los Angeles
Los Angeles, United States
21 h-index
66 publications
21.9K citations
University of Washington
Seattle, United States
21 h-index
50 publications
12.7K citations
Aalborg University
Aalborg, Denmark
20 h-index
38 publications
3.2K citations
Microsoft (United States)
Redmond, United States
19 h-index
45 publications
17.1K citations
University of California, Berkeley
Berkeley, United States
17 h-index
36 publications
3.2K citations
Utrecht University
Utrecht, The Netherlands
17 h-index
40 publications
1.4K citations

University of Warwick
Coventry, United Kingdom
16 h-index
33 publications
1.3K citations
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Repository · 147 publications · 12.3K citations
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62 publications · 2.6K citations
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19 publications · 6.8K citations
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19 publications · 4.6K citations
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15 publications · 1.7K citations
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