State-Space Inference and Learning with Gaussian Processes
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.
Cite
Text
Turner et al. "State-Space Inference and Learning with Gaussian Processes." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Turner et al. "State-Space Inference and Learning with Gaussian Processes." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/turner2010aistats-statespace/)BibTeX
@inproceedings{turner2010aistats-statespace,
title = {{State-Space Inference and Learning with Gaussian Processes}},
author = {Turner, Ryan and Deisenroth, Marc and Rasmussen, Carl},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {868-875},
volume = {9},
url = {https://mlanthology.org/aistats/2010/turner2010aistats-statespace/}
}