Web: http://arxiv.org/abs/2106.10905

Jan. 27, 2022, 2:11 a.m. | Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

cs.LG updates on arXiv.org arxiv.org

Recent machine learning advances have proposed black-box estimation of
unknown continuous-time system dynamics directly from data. However, earlier
works are based on approximative ODE solutions or point estimates. We propose a
novel Bayesian nonparametric model that uses Gaussian processes to infer
posteriors of unknown ODE systems directly from data. We derive sparse
variational inference with decoupled functional sampling to represent vector
field posteriors. We also introduce a probabilistic shooting augmentation to
enable efficient inference from arbitrarily long trajectories. The method …

arxiv bayesian processes

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