A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
From MaRDI portal
Publication:6329844
arXiv1911.11082MaRDI QIDQ6329844
Author name not available (Why is that?)
Publication date: 25 November 2019
Abstract: This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems. We propose formulations to represent, compare, and propagate uncertainties for fairly general stochastic dynamics in a distribution-free manner. The new tools enjoy sound theory rooted in functional analysis and wide applicability as demonstrated in distinct numerical examples. The implication of this new concept is a new mode of thinking about the statistical nature of uncertainty in dynamical systems.
Has companion code repository: https://github.com/jj-zhu/leibniz-ss-2021
This page was built for publication: A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6329844)
