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 …

Gradient matching is a promising tool for learning parameters and state dynamics of
ordinary differential equations. It is a grid free inference approach, which, for fully observable …

Gradient matching with Gaussian processes is a promising tool for learning parameters of
ordinary differential equations (ODE's). The essence of gradient matching is to model the …

Diffusion-and functional MRI are promising avenues for revealing functional organization in
the living human brain since they provide noninvasive measurements pertaining to the …

Gradient matching is a promising tool for learning parameters and state dynamics of
ordinary differential equations. It is a grid free inference approach which for fully observable …

Inferring Non-linear State Dynamics using Gaussian Processes - Research Collection
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