Corpus ID: 17830881

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

@inproceedings{Svensson2016ComputationallyEB,
  title={Computationally Efficient Bayesian Learning of Gaussian Process State Space Models},
  author={Andreas Svensson and A. Solin and Simo S{\"a}rkk{\"a} and Thomas Bo Sch{\"o}n},
  booktitle={AISTATS},
  year={2016}
}
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… Expand
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