Bayesian Change-point Detection

Bayesian Change-Point Detection is a statistical methodology within sequential Bayesian inference that identifies abrupt changes in the generative parameters of a time series through probabilistic updating of change-point occurrences using prior-to-posterior belief propagation.

262

Publications

13.4K

Citations

659

Authors

376

Institutions

Publications per year

2017–2026

68

Authors

659

  1. TB
    Taha B. M. J. Ouarda

    Institut National de la Recherche Scientifique, Canada

    5 publications · 223 citations

    5

    h-index

  2. AG
    Alexander G. Tartakovsky

    University of Southern California, United States

    4 publications · 352 citations

    4

    h-index

  3. OÜ
    O. Üreten

    Communications Research Centre Canada, Canada

    4 publications · 254 citations

    4

    h-index

  4. NS
    N. Serinken

    Communications Research Centre Canada, Canada

    4 publications · 254 citations

    4

    h-index

  5. OS
    Ousmane Seidou

    University of Ottawa, Canada

    4 publications · 206 citations

    4

    h-index

1–5 of 659

Venues

  1. Journal of Climate

    6 publications · 327 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 114 citations

    6

    h-index

  3. Sequential Analysis

    5 publications · 238 citations

    5

    h-index

  4. Water Resources Research

    5 publications · 239 citations

    5

    h-index

  5. 5

    h-index

1–5