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 1532-4435Full text not available from this repository.
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.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||18 May 2016 18:09|
|Last Modified:||29 Aug 2016 01:07|