State-Space Inference and Learning with Gaussian Processes

Turner, R; Deisenroth, M; Rasmussen, CE

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State-Space Inference and Learning with Gaussian Processes

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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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http://proceedings.mlr.press/v9/turner10a/turner10a.pdf
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Citation

Turner, R., Deisenroth, M., & Rasmussen, C. (2010). State-Space Inference and Learning with Gaussian Processes. In Y. Teh, & M. Titterington (Eds.), JMLR Workshop and Conference Proceedings (pp. 868-875). Madison, WI, USA: JMLR.

Cite as: https://hdl.handle.net/21.11116/0000-0002-81F5-7

Abstract

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model.