Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Computation
2016-04-18 v2 Machine Learning
Abstract
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.
Cite
@article{arxiv.1506.02267,
title = {Computationally Efficient Bayesian Learning of Gaussian Process State Space Models},
author = {Andreas Svensson and Arno Solin and Simo Särkkä and Thomas B. Schön},
journal= {arXiv preprint arXiv:1506.02267},
year = {2016}
}