Mixture Priors

Mixture Priors is a prior specification method in Bayesian statistics that belongs to the class of hierarchical priors and is distinguished by its construction as a weighted combination of multiple component distributions to flexibly represent complex prior beliefs or account for uncertainty in prior knowledge.

75

Publications

6K

Citations

176

Authors

107

Institutions

Publications per year

2017–2026

17

Authors

176

  1. DB
    David B. Dunson

    Duke University, United States

    5 publications · 774 citations

    5

    h-index

  2. MV
    Marina Vannucci

    Rice University, United States

    4 publications · 433 citations

    4

    h-index

  3. AB
    Anirban Bhattacharya

    Texas A&M University, United States

    3 publications · 455 citations

    3

    h-index

  4. GL
    Gary L. Rosner

    Johns Hopkins University, United States

    3 publications · 164 citations

    3

    h-index

  5. DP
    Debdeep Pati

    Texas A&M University, United States

    2 publications · 445 citations

    2

    h-index

1–5 of 176

Venues

  1. Biometrics

    7 publications · 1K citations

    7

    h-index

  2. The Annals of Statistics

    5 publications · 358 citations

    5

    h-index

  3. Journal of the American Statistical Association

    5 publications · 1.9K citations

    5

    h-index

  4. Statistics in Medicine

    4 publications · 190 citations

    4

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 86 citations

    3

    h-index

1–5