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

Publications

21.8K

Citations

450

Authors

179

Institutions

Publications per year

2017–2026

97

Authors

450

  1. DM
    David M. Blei

    Princeton University, United States

    16 publications · 2.2K citations

    15

    h-index

  2. RM
    Rafael Molina

    Universidad de Granada, Spain

    10 publications · 882 citations

    10

    h-index

  3. MW
    Max Welling

    University of Amsterdam, Netherlands

    10 publications · 1.3K citations

    10

    h-index

  4. AK
    Aggelos K. Katsaggelos

    Northwestern University, United States

    10 publications · 882 citations

    10

    h-index

  5. RR
    Rajesh Ranganath

    Princeton University, United States

    8 publications · 339 citations

    8

    h-index

1–5 of 450

Venues

  1. arXiv (Cornell University)

    Repository · 76 publications · 5.8K citations

    33

    h-index

  2. Neural Information Processing Systems

    Conference · 11 publications · 939 citations

    10

    h-index

  3. IEEE Transactions on Signal Processing

    7 publications · 365 citations

    7

    h-index

  4. IEEE Transactions on Image Processing

    5 publications · 684 citations

    5

    h-index

  5. 4

    h-index

1–5