Latent Variable Graphical Models
Latent Variable Graphical Models is a class of probabilistic graphical models that incorporate unobserved (latent) variables to represent hidden structures or factors underlying observed data, thereby extending standard graphical models by explicitly modeling dependencies between observed variables through these latent components.
184
Publications
15.4K
Citations
459
Authors
199
Institutions
Publications per year
2017–2026
60
Variants
Latent Variable Graphical Modeling
Parents
184
Mixed Membership Stochastic Blockmodels.
Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg et al. · PubMed · 2008 · 1.5K citations · Full text
Mixed membership stochastic blockmodels
Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg et al. · arXiv (Cornell University) · 2007 · 800 citations · Full text
Hudson Golino, Dingjing Shi, Alexander P. Christensen et al. · Psychological Methods · 2020 · 524 citations · Full text
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199
University of Washington
Seattle, United States
11 h-index
17 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
28 publications
2.7K citations

University of Amsterdam
Amsterdam, The Netherlands
7 h-index
15 publications
2.1K citations
Princeton University
Princeton, United States
6 h-index
10 publications
3.4K citations
University of Oxford
Oxford, United Kingdom
5 h-index
15 publications
388 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
16 publications
4.1K citations
Harvard University
Cambridge, United States
5 h-index
9 publications
178 citations
Stanford University
Stanford, United States
5 h-index
7 publications
258 citations
1–8 of 199
Repository · 21 publications · 1.6K citations
16
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7 publications · 949 citations
7
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TPAMI · 5 publications · 760 citations
5
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5 publications · 488 citations
5
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AOAS · 4 publications · 312 citations
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