Bayesian Latent Variable Models

Bayesian Latent Variable Model is a statistical framework that belongs to the class of Bayesian hierarchical models and is distinguished by its incorporation of unobserved (latent) variables to represent underlying structures or heterogeneity, with inference performed through prior specification and posterior updating via Bayes’ theorem.

592

Publications

35.3K

Citations

1.5K

Authors

698

Institutions

Publications per year

2017–2026

200

Authors

1.5K

  1. DB
    David B. Dunson

    Duke University, United States

    15 publications · 1.7K citations

    13

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    8 publications · 364 citations

    8

    h-index

  3. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    7 publications · 341 citations

    7

    h-index

  4. SD
    Sarah Depaoli

    University of California, Merced, United States

    7 publications · 228 citations

    7

    h-index

  5. RV
    Rens van de Schoot

    Utrecht University, Netherlands

    7 publications · 336 citations

    7

    h-index

1–5 of 1.5K

Venues

  1. Biometrics

    23 publications · 853 citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 21 publications · 767 citations

    16

    h-index

  3. Statistics in Medicine

    18 publications · 507 citations

    15

    h-index

  4. Multivariate Behavioral Research

    MBR · 15 publications · 498 citations

    14

    h-index

  5. Journal of the American Statistical Association

    16 publications · 3.9K citations

    13

    h-index

1–5