Bayesian Experimental Design

Bayesian Experimental Design is a statistical framework for planning experiments in which the choice of design is guided by Bayesian principles to optimize the expected utility of the information gained, distinguishing it from frequentist experimental designs by its incorporation of prior beliefs and probabilistic inference.

782

Publications

54K

Citations

1.6K

Authors

729

Institutions

Publications per year

2017–2026

231

Authors

1.6K

  1. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    22 publications · 4.9K citations

    18

    h-index

  2. PG
    Peter Goos

    University of Antwerp, Belgium

    21 publications · 1.1K citations

    17

    h-index

  3. HD
    Holger Dette

    Ruhr University Bochum, Germany

    14 publications · 476 citations

    14

    h-index

  4. MV
    Martina Vandebroek

    KU Leuven, Belgium

    15 publications · 785 citations

    13

    h-index

  5. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    13 publications · 1.1K citations

    12

    h-index

1–5 of 1.6K

Venues

  1. Statistics in Medicine

    51 publications · 2.7K citations

    28

    h-index

  2. Technometrics

    33 publications · 2.4K citations

    23

    h-index

  3. Biometrics

    28 publications · 1.9K citations

    20

    h-index

  4. Journal of the American Statistical Association

    33 publications · 2.5K citations

    20

    h-index

  5. Journal of Statistical Planning and Inference

    24 publications · 1.6K citations

    17

    h-index

1–5