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stat.ML2013
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dyna…
stat.ML2013★ 82 cited
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
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 inf…