Concepedia

Bayesian Precision-based Sample Size

Bayesian Precision-Based Sample Size is a formal statistical methodology for determining the minimum sample size required in a Bayesian analysis to achieve a pre-specified level of precision in posterior parameter estimation, typically quantified through posterior credible interval width or related probabilistic criteria.

3

Publications

76

Citations

6

Authors

4

Institutions

Publications per year

2017–2026

0

Parents

Publications

3

1–3 of 3

Authors

6

  1. BT
    Blaza Toman

    National Institute of Standards and Technology, United States

    1 publications · 23 citations

    1

    h-index

  2. SA
    Susan Alber

    Oregon State University, United States

    1 publications · 32 citations

    1

    h-index

  3. JL
    Jane L. Hutton

    University of Warwick, United Kingdom

    1 publications · 21 citations

    1

    h-index

  4. RG
    R. Glynn Owens

    University of Auckland, New Zealand

    1 publications · 21 citations

    1

    h-index

  5. JC
    Jing Cao

    Lanzhou University, China

    1 publications · 32 citations

    1

    h-index

1–5 of 6

Institutions

4

  1. George Washington University

    Washington, United States

    1 h-index

    2 publications

    46 citations

  2. Oregon State University

    Corvallis, United States

    1 h-index

    1 publications

    32 citations

  3. The University of Texas MD Anderson Cancer Center

    Houston, United States

    1 h-index

    1 publications

    32 citations

  4. Southern Methodist University

    Dallas, United States

    1 h-index

    1 publications

    32 citations

1–4 of 4

Venues

  1. Journal of Statistical Planning and Inference

    2 publications · 55 citations

    2

    h-index

  2. 1

    h-index

1–2