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 learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present extensions to stochastic variational inference and online learning.
In REF a variational GP model is presented for nonlinear state-space models.
Variational Gaussian Process State-Space Models
R. Frigola, Y. Chen and C. E. Rasmussen. Variational Gaussian Process State-Space Models, in Advances in Neural Information Processing Systems (NIPS), 2014
Computer Science Mathematics