Simulation-Free Learning of GP-SDEs from Irregular Observations
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learning and inference from noisy and irregular observations remain computationally challenging. To address this issue, we propose GP-SDE Matchin...
Zhi-Di Lin, Yu-Hao Liu, Ying Li et al.
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