Variational Bayesian Inference
Variational Bayesian Inference is a method for approximating intractable posterior distributions in Bayesian models by formulating the inference problem as an optimization task that minimizes the Kullback-Leibler divergence between the true posterior and a tractable variational distribution.
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907
Institutions
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2017–2026
680
1.3K
Auto-Encoding Variational Bayes
Diederik P. Kingma, Max Welling · UvA-DARE (University of Amsterdam) · 2013 · 15.6K citations · Full text
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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University College London
London, United Kingdom
30 h-index
79 publications
8.5K citations

Harbin Engineering University
Harbin, China
23 h-index
89 publications
9.8K citations
Duke University
Durham, United States
22 h-index
91 publications
5.1K citations

University of Edinburgh
Edinburgh, United Kingdom
20 h-index
47 publications
2.3K citations
University of Oxford
Oxford, United Kingdom
20 h-index
65 publications
4.4K citations
University of California, Berkeley
Berkeley, United States
19 h-index
73 publications
6.8K citations
Wellcome Centre for Human Neuroimaging
London, United Kingdom
18 h-index
50 publications
5.8K citations

University of Amsterdam
Amsterdam, The Netherlands
17 h-index
31 publications
34.3K citations
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Repository · 174 publications · 11.2K citations
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47 publications · 3.6K citations
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TPAMI · 20 publications · 1.9K citations
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Conference · 16 publications · 490 citations
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17 publications · 957 citations
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