PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

State-Space Inference and Learning with Gaussian Processes
Ryan Turner, Marc Deisenroth and Carl Edward Rasmussen
In: AISTATS 2010, 13-15 May 2010, Sardinia, Italy.


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.

PDF - Archive staff only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6629
Deposited By:Ryan Turner
Deposited On:08 March 2010