Variational Bayes

Variational Bayes is a variational inference method that approximates intractable posterior distributions in Bayesian models by framing posterior estimation as an optimization problem over a family of tractable distributions.

1.3K

Publications

110.1K

Citations

2.6K

Authors

867

Institutions

Publications per year

2017–2026

535

Authors

2.6K

  1. DM
    David M. Blei

    Princeton University, United States

    25 publications · 4.4K citations

    20

    h-index

  2. MW
    Max Welling

    University of Amsterdam, Netherlands

    24 publications · 18.2K citations

    20

    h-index

  3. KF
    Karl Friston

    University College London, United Kingdom

    20 publications · 3.2K citations

    19

    h-index

  4. LC
    Lawrence Carin

    Duke University, United States

    22 publications · 988 citations

    17

    h-index

  5. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    19 publications · 4.3K citations

    17

    h-index

1–5 of 2.6K

Venues

  1. arXiv (Cornell University)

    Repository · 275 publications · 16.9K citations

    61

    h-index

  2. IEEE Transactions on Signal Processing

    26 publications · 1.9K citations

    20

    h-index

  3. Neural Information Processing Systems

    Conference · 31 publications · 1.5K citations

    18

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 19 publications · 3.7K citations

    18

    h-index

  5. Neural Computation

    17 publications · 1.7K citations

    15

    h-index

1–5