CUED Publications database

State-space inference and learning with Gaussian processes

Turner, R and Deisenroth, MP and Rasmussen, CE (2010) State-space inference and learning with Gaussian processes. Journal of Machine Learning Research, 9. pp. 868-875. ISSN 1532-4435

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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. Copyright 2010 by the authors.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron job
Date Deposited: 04 Feb 2015 23:03
Last Modified: 22 Jun 2015 01:22
DOI: