Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
2016 (English)In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016, 213-221 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2016. 213-221 p.
Electrical Engineering, Electronic Engineering, Information Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:uu:diva-307407OAI: oai:DiVA.org:uu-307407DiVA: diva2:1046826
the 19th International Conference on Artificial Intelligence and Statistics (AISTATS)