Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system without explicitly solving it. While the benefits in computational cost are well established, the theoretical foundation has been criticized in the past. We offer a novel interpretation which leads to a better understanding and improvements in state-of-the-art performance in terms of accuracy, robustness and a decrease in run time due to a more efficient setup for general nonlinear dynamical systems.

@inproceedings{wenk18fgpgm,
	author = {Wenk, Philippe and Gotovos, Alkis and Bauer, Stefan and Gorbach, Nico S. and Krause, Andreas and Buhmann, Joachim M.},
	booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
	month = {April},
	title = {Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs},
	year = {2019}}