Variational Bayes Inference
Variational Bayes Inference is a methodological framework within approximate Bayesian inference that formulates posterior approximation as an optimization problem by minimizing the Kullback-Leibler divergence between a tractable variational distribution and the true posterior distribution.
771
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72.6K
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1.8K
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628
Institutions
Publications per year
2017–2026
352
771
Auto-Encoding Variational Bayes
Diederik P. Kingma, Max Welling · UvA-DARE (University of Amsterdam) · 2013 · 15.6K citations · Full text
<i>Stan</i> : A Probabilistic Programming Language
Bob Carpenter, Andrew Gelman, Matthew D. Hoffman et al. · Journal of Statistical Software · 2017 · 7.4K 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
22 h-index
51 publications
5K citations
University of Oxford
Oxford, United Kingdom
18 h-index
48 publications
4.2K citations
University of California, Berkeley
Berkeley, United States
17 h-index
48 publications
2.9K citations
University of Cambridge
Cambridge, United Kingdom
16 h-index
42 publications
2.2K citations

University of Amsterdam
Amsterdam, The Netherlands
16 h-index
29 publications
34K citations
University of Technology Sydney
Sydney, Australia
15 h-index
26 publications
1.6K citations
Duke University
Durham, United States
15 h-index
55 publications
2.6K citations
Wellcome Centre for Human Neuroimaging
London, United Kingdom
14 h-index
31 publications
2.5K citations
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Repository · 150 publications · 8.8K citations
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Conference · 21 publications · 1.3K citations
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19 publications · 1.5K citations
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Conference · 10 publications · 206 citations
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TPAMI · 9 publications · 563 citations
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