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Bayesian Online Prediction of Change Points

Diego Agudelo-Espa~na; Sebastián Gómez-González; Stefan Bauer; Bernhard Schölkopf; Jan Peters
In: Ryan P. Adams; Vibhav Gogate (Hrsg.). Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence. Conference in Uncertainty in Artificial Intelligence (UAI-2020), August 3-6, Pages 320-329, Proceedings of Machine Learning Research, Vol. 124, AUAI Press, 2020.


Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.

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