Bayesian Optimal Stopping

Bayesian Optimal Stopping is an optimal stopping framework that incorporates Bayesian inference to sequentially update beliefs about unknown parameters and determine the optimal stopping rule that maximizes expected utility under uncertainty.

80

Publications

3.8K

Citations

143

Authors

77

Institutions

Publications per year

2017–2026

11

Authors

143

  1. AN
    Albert N. Shiryaev

    Steklov Mathematical Institute, Russia

    7 publications · 241 citations

    7

    h-index

  2. PV
    Pavel V. Gapeev

    London School of Economics and Political Science, United Kingdom

    4 publications · 110 citations

    4

    h-index

  3. SE
    Stephen E. Chick

    INSEAD, France

    4 publications · 352 citations

    4

    h-index

  4. JA
    J. A. Bather

    University of Sussex, United Kingdom

    3 publications · 225 citations

    3

    h-index

  5. CS
    Christian Schmidt

    INSEAD, France

    3 publications · 313 citations

    3

    h-index

1–5 of 143

Venues

  1. The Annals of Statistics

    8 publications · 839 citations

    8

    h-index

  2. IEEE Transactions on Information Theory

    4 publications · 144 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 168 citations

    4

    h-index

  4. Sequential Analysis

    3 publications · 105 citations

    3

    h-index

  5. The Annals of Applied Probability

    3 publications · 57 citations

    3

    h-index

1–5