Bayesian Sequential Testing

Bayesian Sequential Testing is a statistical decision framework belonging to the class of sequential analysis methods, distinguished by its incorporation of Bayesian updating to sequentially evaluate hypotheses as data accrue, enabling dynamic termination of experimentation based on posterior probability thresholds.

126

Publications

6.3K

Citations

272

Authors

169

Institutions

Publications per year

2017–2026

20

Authors

272

  1. SD
    Savaş Dayanik

    Princeton University, United States

    4 publications · 121 citations

    4

    h-index

  2. HV
    H. Vincent Poor

    Princeton University, United States

    4 publications · 297 citations

    4

    h-index

  3. VV
    Venugopal V. Veeravalli

    University of Illinois Urbana-Champaign, United States

    4 publications · 801 citations

    4

    h-index

  4. MP
    Marco Perugini

    University of Milano-Bicocca, Italy

    3 publications · 652 citations

    3

    h-index

  5. RO
    Roger Owen

    Novartis (United Kingdom), United Kingdom

    3 publications · 379 citations

    3

    h-index

1–5 of 272

Venues

  1. Journal of the American Statistical Association

    11 publications · 688 citations

    11

    h-index

  2. IEEE Transactions on Information Theory

    9 publications · 973 citations

    9

    h-index

  3. The Annals of Mathematical Statistics

    8 publications · 539 citations

    8

    h-index

  4. Biometrika

    5 publications · 458 citations

    5

    h-index

  5. The Annals of Statistics

    4 publications · 214 citations

    4

    h-index

1–5