Variational Posterior Approximation
Variational Posterior Approximation is a methodological framework in Bayesian inference that approximates the intractable posterior distribution by optimizing a simpler, parametric distribution to minimize the divergence from the true posterior.
204
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21.8K
Citations
450
Authors
179
Institutions
Publications per year
2017–2026
97
Parents
204
Stochastic variational inference
Matthew D. Hoffman, David M. Blei, Chong Wang et al. · Journal of Machine Learning Research · 2013 · 1.5K citations
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende, Shakir Mohamed · arXiv (Cornell University) · 2015 · 1.4K citations · Full text
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University of Cambridge
Cambridge, United Kingdom
12 h-index
26 publications
2.1K citations
Columbia University
New York, United States
12 h-index
23 publications
1.1K citations
Princeton University
Princeton, United States
10 h-index
15 publications
2.1K citations
Northwestern University
Evanston, United States
10 h-index
18 publications
1.5K citations

Universidad de Granada
Granada, Spain
10 h-index
17 publications
1.3K citations
Stanford University
Stanford, United States
9 h-index
19 publications
5.2K citations
University of California, Berkeley
Berkeley, United States
8 h-index
16 publications
2.4K citations
Google (United States)
Mountain View, United States
8 h-index
21 publications
1.2K citations
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Repository · 76 publications · 5.8K citations
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Conference · 11 publications · 939 citations
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7 publications · 365 citations
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5 publications · 684 citations
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TPAMI · 4 publications · 800 citations
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