Derivative Observations in Gaussian Process Models of Dynamic Systems

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium.