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.