A New Distribution-Free Concept for Representing, Comparing, and Propagating 
Uncertainty in Dynamical Systems with Kernel Probabilistic Programming

Zhu, Jia-Jie; Muandet, Krikamol; Diehl, Moritz; Schölkopf, Bernhard

doi:10.1016/j.ifacol.2020.12.557

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A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming

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Zhu, Jia-Jie
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Muandet, Krikamol
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf, Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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https://doi.org/10.48550/arXiv.1911.11082
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https://doi.org/10.1016/j.ifacol.2020.12.557
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Zhu, J.-J., Muandet, K., Diehl, M., & Schölkopf, B. (2021). A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. IFAC-PapersOnLine, 53(2), 7240-7247. doi:10.1016/j.ifacol.2020.12.557.

Cite as: https://hdl.handle.net/21.11116/0000-000B-2DE4-2

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