State-Space Inference and Learning with Gaussian Processes
Ryan Turner, Marc Deisenroth, Carl Rasmussen ; JMLR W&CP 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.
