Variational Gaussian Process State-Space Models
This work provides a more efficient and scalable approach for modeling nonlinear dynamical systems, which is incremental as it builds on existing state-space and Gaussian process methods.
The authors tackled the problem of learning nonlinear state-space models by introducing a variational Bayesian method using sparse Gaussian processes, resulting in a tractable posterior that allows flexible trade-offs between model capacity and computational cost while avoiding overfitting.
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 stochastic variational inference and online learning approaches for fast learning with long time series.