Gaussian Variational Approximation

Gaussian Variational Approximation is a variational inference method that employs a Gaussian distribution as the approximating family to model the posterior distribution, distinguished by its assumption of normality and dependence on mean and covariance parameters for tractable Bayesian inference.

186

Publications

10.8K

Citations

404

Authors

220

Institutions

Publications per year

2017–2026

56

Authors

404

  1. TG
    Thierry Giamarchi

    University of Geneva, Switzerland

    6 publications · 1.3K citations

    6

    h-index

  2. GA
    Graham A. Worth

    University of Birmingham, United Kingdom

    6 publications · 728 citations

    6

    h-index

  3. VA
    Vladimir A. Mandelshtam

    University of California, Irvine, United States

    6 publications · 247 citations

    6

    h-index

  4. PL
    Pierre Le Doussal

    Centre National de la Recherche Scientifique, France

    5 publications · 862 citations

    5

    h-index

  5. IB
    Irène Burghardt

    Goethe University Frankfurt, Germany

    5 publications · 703 citations

    5

    h-index

1–5 of 404

Venues

  1. The Journal of Chemical Physics

    15 publications · 557 citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 12 publications · 817 citations

    11

    h-index

  3. 10

    h-index

  4. Physical Review A

    PRA · 8 publications · 461 citations

    8

    h-index

  5. Physical Review Letters

    PRL · 7 publications · 926 citations

    7

    h-index

1–5