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  State-Space Inference and Learning with Gaussian Processes

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.

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 Creators:
Turner, R, Author
Deisenroth, M, Author           
Rasmussen, CE1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Place of Event: Chia Laguna Resort, Sardinia, Italy
Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Proceedings
 Creator(s):
Teh, YW, Editor
Titterington , M, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : JMLR
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 868 - 875 Identifier: -