State-Space Inference and Learning with Gaussian Processes

Ryan Turner, Marc Deisenroth, Carl Rasmussen
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:868-875, 2010.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-turner10a, title = {State-Space Inference and Learning with Gaussian Processes}, author = {Turner, Ryan and Deisenroth, Marc and Rasmussen, Carl}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {868--875}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/turner10a/turner10a.pdf}, url = {https://proceedings.mlr.press/v9/turner10a.html}, 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.} }
Endnote
%0 Conference Paper %T State-Space Inference and Learning with Gaussian Processes %A Ryan Turner %A Marc Deisenroth %A Carl Rasmussen %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-turner10a %I PMLR %P 868--875 %U https://proceedings.mlr.press/v9/turner10a.html %V 9 %X 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.
RIS
TY - CPAPER TI - State-Space Inference and Learning with Gaussian Processes AU - Ryan Turner AU - Marc Deisenroth AU - Carl Rasmussen BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-turner10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 868 EP - 875 L1 - http://proceedings.mlr.press/v9/turner10a/turner10a.pdf UR - https://proceedings.mlr.press/v9/turner10a.html AB - 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. ER -
APA
Turner, R., Deisenroth, M. & Rasmussen, C.. (2010). State-Space Inference and Learning with Gaussian Processes. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:868-875 Available from https://proceedings.mlr.press/v9/turner10a.html.

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