Corpus ID: 67856716

Bayesian Anomaly Detection and Classification

@article{Roberts2019BayesianAD,
  title={Bayesian Anomaly Detection and Classification},
  author={Ethan Roberts and Bruce A. Bassett and Michelle Lochner},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.08627}
}
Statistical uncertainties are rarely incorporated in machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise, BADAC is shown to be superior to… Expand
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