Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2015
- DOI:
- 10.48550/arXiv.1506.02267
- arXiv:
- arXiv:1506.02267
- Bibcode:
- 2015arXiv150602267S
- Keywords:
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- Statistics - Computation;
- Statistics - Machine Learning