Polya-gamma Data Augmentation

Polya-Gamma Data Augmentation is a Bayesian computational method that belongs to the class of data augmentation techniques, distinguished by its use of latent variables drawn from the Polya-Gamma distribution to enable conjugate updating in logistic and related generalized linear models, thereby facilitating efficient Gibbs sampling for posterior inference.

38

Publications

2.2K

Citations

95

Authors

50

Institutions

Publications per year

2017–2026

22

Variants

Pólya-gamma Data Augmentation

Authors

95

  1. SW
    Scott W. Linderman

    Stanford University, United States

    7 publications · 198 citations

    7

    h-index

  2. JG
    James G. Scott

    The University of Texas at Austin, United States

    4 publications · 103 citations

    4

    h-index

  3. RP
    Ryan P. Adams

    Harvard University, United States

    4 publications · 128 citations

    4

    h-index

  4. NG
    Nicholas G. Polson

    University of Chicago, United States

    3 publications · 38 citations

    3

    h-index

  5. JW
    Jonathan W. Pillow

    Princeton University, United States

    3 publications · 117 citations

    3

    h-index

1–5 of 95

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 211 citations

    9

    h-index

  2. Neural Information Processing Systems

    Conference · 3 publications · 101 citations

    3

    h-index

  3. The Annals of Applied Statistics

    AOAS · 2 publications · 70 citations

    2

    h-index

  4. Electronic Journal of Statistics

    2 publications · 120 citations

    2

    h-index

  5. Psychometrika

    2 publications · 44 citations

    2

    h-index

1–5