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Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Roger Frigola · Fredrik Lindsten · Thomas Schön · Carl Edward Rasmussen

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.

Author Information

Roger Frigola (University of Cambridge)
Fredrik Lindsten (Linköping University)
Thomas Schön (Uppsala University)
Carl Edward Rasmussen (University of Cambridge)

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