Bayesian Probit Models

Bayesian Probit Models is a Bayesian statistical model that extends the probit regression framework by incorporating prior distributions on parameters to estimate latent binary outcomes through the assumption of an underlying normal distribution.

149

Publications

14K

Citations

327

Authors

202

Institutions

Publications per year

2017–2026

26

Authors

327

  1. SC
    Siddhartha Chib

    Washington University in St. Louis, United States

    5 publications · 4.5K citations

    5

    h-index

  2. DB
    David B. Dunson

    Duke University, United States

    4 publications · 235 citations

    4

    h-index

  3. RE
    Robert E. McCulloch

    University of Chicago, United States

    3 publications · 501 citations

    3

    h-index

  4. MH
    Matthew Harding

    Stanford University, United States

    3 publications · 159 citations

    3

    h-index

  5. JG
    John Geweke

    University of Technology Sydney, Australia

    3 publications · 243 citations

    3

    h-index

1–5 of 327

Venues

  1. Journal of the American Statistical Association

    10 publications · 4.1K citations

    10

    h-index

  2. Journal of Econometrics

    7 publications · 877 citations

    7

    h-index

  3. Technometrics

    4 publications · 1K citations

    4

    h-index

  4. SSRN Electronic Journal

    Repository · 4 publications · 135 citations

    4

    h-index

  5. Bayesian Analysis

    3 publications · 219 citations

    3

    h-index

1–5