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.

1.3K

Publications

92K

Citations

3.1K

Authors

907

Institutions

Publications per year

2017–2026

680

Authors

3.1K

  1. YH
    Yulong Huang

    Harbin Engineering University, China

    19 publications · 2.6K citations

    19

    h-index

  2. YZ
    Yonggang Zhang

    Harbin Engineering University, China

    18 publications · 2.7K citations

    18

    h-index

  3. MW
    Max Welling

    University of Amsterdam, Netherlands

    18 publications · 18.4K citations

    16

    h-index

  4. LC
    Lawrence Carin

    Duke University, United States

    18 publications · 1K citations

    16

    h-index

  5. DM
    David M. Blei

    Princeton University, United States

    18 publications · 4.8K citations

    15

    h-index

1–5 of 3.1K

Venues

  1. arXiv (Cornell University)

    Repository · 174 publications · 11.2K citations

    48

    h-index

  2. IEEE Transactions on Signal Processing

    47 publications · 3.6K citations

    33

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 20 publications · 1.9K citations

    16

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 16 publications · 490 citations

    15

    h-index

  5. 15

    h-index

1–5