# Bayesian Histogram Anomaly Detection

Python package of the *Bayesian Histogram-based Anomaly Detection (BHAD)* algorithm as presented in [Vosseler, A. (2022): Unsupervised Insurance Fraud Prediction Based on Anomaly Detector Ensembles](https://www.researchgate.net/publication/361463552_Unsupervised_Insurance_Fraud_Prediction_Based_on_Anomaly_Detector_Ensembles) and [Vosseler, A. (2023): BHAD: Explainable anomaly detection using Bayesian histograms](https://www.researchgate.net/publication/364265660_BHAD_Explainable_anomaly_detection_using_Bayesian_histograms). The latter reference focuses also on the explainabilty aspects of the approach, namely being globally and locally explainable due to its linear structure.  

This work was also presented at *PyCon DE & PyData Berlin 2023* ([watch talk here](https://www.youtube.com/watch?v=_8zfgPTD-d8&list=PLGVZCDnMOq0peDguAzds7kVmBr8avp46K&index=8)) and at the *42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering* ([MaxEnt 2023](https://www.mdpi.com/2673-9984/9/1/1)), at Max-Planck-Institute for Plasma Physics, Garching, Germany.