• Corpus ID: 54462057

Closed-form Inference and Prediction in Gaussian Process State-Space Models

@article{Ialongo2018ClosedformIA,
  title={Closed-form Inference and Prediction in Gaussian Process State-Space Models},
  author={Alessandro Davide Ialongo and Mark van der Wilk and Carl Edward Rasmussen},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.03580}
}
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling. Our approach performs variational inference over both the system states and the transition function. We exploit Markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series. Contrary to previous approaches, no Monte Carlo sampling… 

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