  Within the past two decades, Gaussian process regression has been
increasingly used for modeling dynamical systems due to some beneficial
properties such as the bias variance trade-off and the strong connection to
Bayesian mathematics. As data-driven method, a Gaussian process is a powerful
tool for nonlinear function regression without the need of much prior
knowledge. In contrast to most of the other techniques, Gaussian Process
modeling provides not only a mean prediction but also a measure for the model
fidelity. In this article, we give an introduction to Gaussian processes and
its usage in regression tasks of dynamical systems. Try Gaussian process
regression yourself: https://gpr.tbeckers.com
