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Conference Paper

Gaussian process change point models

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Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Saatci, Y., Turner, R., & Rasmussen, C. (2010). Gaussian process change point models. In J. Fürnkranz, & T. Joachims (Eds.), 27th International Conference on Machine Learning (ICML 2010) (pp. 927-934). Madison, WI, USA: Omnipress.


Cite as: https://hdl.handle.net/21.11116/0000-0002-81DC-4
Abstract
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.