Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola 1, Thomas B. Schön 2,3 and Carl E. Rasmussen

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 (i.e.