Robust and Scalable Bayesian Online Changepoint Detection

This repository contains all code and data needed to reproduce the results in the paper "Robust and Scalable Bayesian Online Changepoint Detection".

Reproducing experiments

• The folder data contains all the datasets used for the experiments.
• The folder notebooks contains notebooks to recreate all the experiments.
• The file models.py contains all the probability models implemented: DSM - Bayes and standard Bayes.
• The file bocpd.py contains the main function to run the algorithm.

Requirements

• Python == 3.9.*
• Numpy == 1.20.3
• SciPy == 1.7.1
• Jax == 0.4.1

Citation

M. Altamirano, F.-X. Briol, and J. Knoblauch, “Robust and scalable Bayesian online changepoint detection”, in Proceedings of the 40th International Conference on Machine Learning, PMLR, 2023, pp. 642–663.