Use of Gaussian Processes in System Identification

Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to form time series prediction models such as nonlinear finite-impulse response (NFIR) models as well as nonlinear autoregressive (NARX) models. Gaussian process state-space (GPSS) models can be used to learn the dynamic and measurement models for a state-space representation of the input-output data. Temporal and spatiotemporal Gaussian processes can be directly used to form regressor on the data in the time domain. The aim of this article is to briefly outline the main directions in system identification methods using Gaussian processes.