Bayesian Anomaly Detection

Bayesian Anomaly Detection is a probabilistic framework for identifying anomalous observations in time series data by modeling uncertainty through Bayesian inference, distinguishing itself from other anomaly detection methods through its principled incorporation of prior knowledge and posterior updating to quantify the probability of anomalies.

191

Publications

11.9K

Citations

563

Authors

287

Institutions

Publications per year

2017–2026

83

Authors

563

  1. LM
    Leonardo M. Millefiori

    North Atlantic Treaty Organization, Belgium

    4 publications · 71 citations

    4

    h-index

  2. NF
    Nicola Forti

    North Atlantic Treaty Organization, Belgium

    4 publications · 71 citations

    4

    h-index

  3. PW
    Peter Willett

    University of Connecticut, United States

    4 publications · 108 citations

    4

    h-index

  4. PB
    Paolo Braca

    North Atlantic Treaty Organization, Belgium

    4 publications · 71 citations

    4

    h-index

  5. GF
    Gregory F. Cooper

    University of Pittsburgh, United States

    3 publications · 264 citations

    3

    h-index

1–5 of 563

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 144 citations

    6

    h-index

  2. Expert Systems with Applications

    4 publications · 96 citations

    4

    h-index

  3. Biometrika

    3 publications · 361 citations

    3

    h-index

  4. Structural Control and Health Monitoring

    3 publications · 108 citations

    3

    h-index

  5. Sensors

    3 publications · 98 citations

    3

    h-index

1–5