126 citations · 208 across the 2 of their papers we have counts for
4 papers
Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen, Carl E. Rasmussen
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian le…
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…
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…
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
Roger Frigola, Carl Edward Rasmussen
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear r…