Bayesian Factor Analysis

Bayesian Factor Analysis is a statistical modeling framework within the class of latent variable models that uses Bayesian inference to estimate latent factors and their loadings by incorporating prior distributions over model parameters, thereby distinguishing itself from classical factor analysis through its probabilistic treatment of uncertainty and parameter estimation.

186

Publications

11K

Citations

488

Authors

307

Institutions

Publications per year

2017–2026

59

Authors

488

  1. CH
    Charles H. Whiteman

    University of Iowa, United States

    4 publications · 1.6K citations

    4

    h-index

  2. MA
    M. Ayhan Köse

    Brookings Institution, United States

    4 publications · 1.6K citations

    4

    h-index

  3. MT
    Michael T. Owyang

    Federal Reserve Bank of St. Louis, United States

    3 publications · 73 citations

    3

    h-index

  4. DB
    David B. Dunson

    Duke University, United States

    3 publications · 626 citations

    3

    h-index

  5. SL
    Sik‐Yum Lee

    Chinese University of Hong Kong, Hong Kong SAR China

    3 publications · 156 citations

    3

    h-index

1–5 of 488

Venues

  1. SSRN Electronic Journal

    Repository · 10 publications · 473 citations

    10

    h-index

  2. Psychometrika

    7 publications · 376 citations

    7

    h-index

  3. Bioinformatics

    4 publications · 290 citations

    4

    h-index

  4. 4

    h-index

  5. arXiv (Cornell University)

    Repository · 4 publications · 124 citations

    4

    h-index

1–5