Hierarchical Bayesian Design

Hierarchical Bayesian Design is a Bayesian adaptive design framework that incorporates multiple levels of uncertainty by structuring prior distributions hierarchically to model dependencies across parameters, thereby enabling information borrowing across subgroups or stages in a principled manner.

90

Publications

7.7K

Citations

474

Authors

280

Institutions

Publications per year

2017–2026

35

Authors

474

  1. MR
    Martin R. Young

    Massey University, New Zealand

    2 publications · 577 citations

    2

    h-index

  2. VL
    Van L. Parsons

    National Center for Health Statistics, United States

    2 publications · 143 citations

    2

    h-index

  3. AF
    Adam F Osth

    The University of Melbourne, Australia

    2 publications · 61 citations

    2

    h-index

  4. FH
    Fang Hou

    Wenzhou Medical University, China

    2 publications · 63 citations

    2

    h-index

  5. PE
    Paul E. Green

    University of Pennsylvania, United States

    2 publications · 577 citations

    2

    h-index

1–5 of 474

Venues

  1. PLoS ONE

    5 publications · 259 citations

    5

    h-index

  2. Statistics in Medicine

    5 publications · 2.8K citations

    5

    h-index

  3. 3

    h-index

  4. Accident Analysis & Prevention

    3 publications · 451 citations

    3

    h-index

  5. Marketing Science

    3 publications · 808 citations

    3

    h-index

1–5