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

Publications

72.6K

Citations

1.8K

Authors

628

Institutions

Publications per year

2017–2026

352

Authors

1.8K

  1. MW
    Max Welling

    University of Amsterdam, Netherlands

    15 publications · 18.1K citations

    15

    h-index

  2. MP
    M. P. Wand

    UNSW Sydney, Australia

    17 publications · 1K citations

    15

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    13 publications · 2.7K citations

    13

    h-index

  4. DM
    David M. Blei

    Princeton University, United States

    14 publications · 2.1K citations

    13

    h-index

  5. NB
    Nizar Bouguila

    Concordia University, Canada

    16 publications · 376 citations

    13

    h-index

1–5 of 1.8K

Venues

  1. arXiv (Cornell University)

    Repository · 150 publications · 8.8K citations

    48

    h-index

  2. Neural Information Processing Systems

    Conference · 21 publications · 1.3K citations

    16

    h-index

  3. IEEE Transactions on Signal Processing

    19 publications · 1.5K citations

    16

    h-index

  4. International Conference on Machine Learning

    Conference · 10 publications · 206 citations

    10

    h-index

  5. 9

    h-index

1–5