Web: http://arxiv.org/abs/2209.10166

Sept. 22, 2022, 1:13 a.m. | Ariel Neufeld, Philipp Schmocker

stat.ML updates on arXiv.org arxiv.org

In this paper, we extend the Wiener-Ito chaos decomposition to the class of
diffusion processes, whose drift and diffusion coefficient are of linear
growth. By omitting the orthogonality in the chaos expansion, we are able to
show that every $p$-integrable functional, for $p \in [1,\infty)$, can be
represented as sum of iterated integrals of the underlying process. Using a
truncated sum of this expansion and (possibly random) neural networks for the
integrands, whose parameters are learned in a machine learning …

arxiv networks neural networks

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