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Parameter identification and comparison of dynamical systems is a challengingtask in many fields. Bayesian approaches based on Gaussian process regressionover time-series data have been successfully applied to infer the parameters ofa dynamical system without explicitly solving it. While the benefits incomputational cost are well established, a rigorous mathematical framework hasbeen missing. We offer a novel interpretation which leads to a betterunderstanding and improvements in state-of-the-art performance in terms ofaccuracy for nonlinear dynamical systems.

Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs