Variational Gaussian Process State-Space Models

no code implementations • NeurIPS 2014 • 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.

Gaussian Processes Time Series +2

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM

no code implementations • 17 Dec 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems.

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

no code implementations • NeurIPS 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems.

Bayesian Inference Gaussian Processes +2

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes

no code implementations • 12 Mar 2013 • 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 regression problem is tackled using sparse Gaussian processes (GP).

Gaussian Processes regression