Bayesian Sequential Design

Bayesian Sequential Design is a statistical decision framework that iteratively updates beliefs about model parameters using Bayesian inference to guide the selection of subsequent experimental conditions in a sequential experimentation process.

154

Publications

7.5K

Citations

465

Authors

260

Institutions

Publications per year

2017–2026

46

Authors

465

  1. AP
    Alexander Penlidis

    University of Waterloo, Canada

    5 publications · 78 citations

    5

    h-index

  2. AN
    Afsaneh Nabifar

    University of Waterloo, Canada

    4 publications · 46 citations

    4

    h-index

  3. FD
    Felix D. Schönbrodt

    Ludwig-Maximilians-Universität München, Germany

    4 publications · 1.1K citations

    4

    h-index

  4. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 441 citations

    4

    h-index

  5. RL
    Roger Lewis

    UCLA Medical Center, United States

    3 publications · 164 citations

    3

    h-index

1–5 of 465

Venues

  1. Journal of the American Statistical Association

    14 publications · 854 citations

    12

    h-index

  2. Statistics in Medicine

    8 publications · 636 citations

    8

    h-index

  3. Biometrika

    7 publications · 544 citations

    7

    h-index

  4. Technometrics

    5 publications · 85 citations

    5

    h-index

  5. Journal of Statistical Planning and Inference

    4 publications · 127 citations

    4

    h-index

1–5