Bayesian Nonparametric Sample Size

Bayesian Nonparametric Sample Size is a statistical methodology that determines the minimum sample size required for a study using Bayesian nonparametric models, which avoid rigid parametric assumptions by placing priors on infinite-dimensional spaces to flexibly model unknown distributions while incorporating decision-theoretic or precision-based criteria for sample size justification.

4

Publications

78

Citations

9

Authors

9

Institutions

Publications per year

2017–2026

0

Authors

9

  1. AL
    Antonio Lijoi

    University of Pavia, Italy

    1 publications · 32 citations

    1

    h-index

  2. RG
    R. Glynn Owens

    University of Auckland, New Zealand

    1 publications · 21 citations

    1

    h-index

  3. RH
    Ramsés H. Mena

    Universidad Nacional Autónoma de México, Mexico

    1 publications · 32 citations

    1

    h-index

  4. WO
    Wesley O. Johnson

    University of California, Davis, United States

    1 publications · 10 citations

    1

    h-index

  5. AJ
    Adam J. Branscum

    Oregon State University, United States

    1 publications · 10 citations

    1

    h-index

1–5 of 9

Venues

  1. Statistics in Medicine

    1 publications · 10 citations

    1

    h-index

  2. 1

    h-index

  3. BMC Bioinformatics

    1 publications · 32 citations

    1

    h-index

  4. 1

    h-index

1–4