State-Space Inference and Learning with Gaussian Processes

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Title: State-Space Inference and Learning with Gaussian Processes
Author(s): Turner, R
Deisenroth, MP
Rasmussen, CE
Item Type: Conference Paper
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. Copyright 2010 by the authors.
Editor(s): Teh, YW
Titterington, M
Publication Date: 1-Dec-2010
URI: http://hdl.handle.net/10044/1/12212
Publisher: JMLR
Start Page: 868
End Page: 875
Journal / Book Title: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Volume: 9
Copyright Statement: © 2010 The Authors
Conference Name: AISTATS 2010
Publisher URL: http://jmlr.org/proceedings/papers/v9/
Start Date: 2010-05-13
Finish Date: 2010-05-15
Conference Place: Sardinia, Italy
Appears in Collections:Computing



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