Latent Variable Graphical Models

Latent Variable Graphical Models is a class of probabilistic graphical models that incorporate unobserved (latent) variables to represent hidden structures or factors underlying observed data, thereby extending standard graphical models by explicitly modeling dependencies between observed variables through these latent components.

184

Publications

15.4K

Citations

459

Authors

199

Institutions

Publications per year

2017–2026

60

Variants

Latent Variable Graphical Modeling

Authors

459

  1. EM
    Edoardo M. Airoldi

    Harvard University, United States

    5 publications · 2.4K citations

    5

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    5 publications · 2.6K citations

    5

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    5 publications · 2.4K citations

    5

    h-index

  4. PD
    Peter D. Hoff

    University of Washington, United States

    4 publications · 602 citations

    4

    h-index

  5. SE
    Stephen E. Fienberg

    Carnegie Mellon University, United States

    4 publications · 2.4K citations

    4

    h-index

1–5 of 459

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 1.6K citations

    16

    h-index

  2. Psychometrika

    7 publications · 949 citations

    7

    h-index

  3. 5

    h-index

  4. The Annals of Statistics

    5 publications · 488 citations

    5

    h-index

  5. The Annals of Applied Statistics

    AOAS · 4 publications · 312 citations

    4

    h-index

1–5