Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

Identification and comparison of nonlinear dynamical system models using noisy and sparse experimental data is a vital task in many fields, however current methods are computationally expensive and prone to error due in part to the nonlinear nature of the likelihood surfaces induced.