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  • title: Higher Order KMEs to capture Filtrations of Stochastic Processes
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            Higher Order KMEs to capture Filtrations of Stochastic Processes
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            Higher Order KMEs to capture Filtrations of Stochastic Processes

            Dec 6, 2021

            Speakers

            CS

            Cristopher Salvi

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            ML

            Maud Lemercier

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            CL

            Chong Liu

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            About

            Stochastic processes are random variables with values in some space of paths. However, reducing a stochastic process to a path-valued random variable ignores its filtration, i.e. the flow of information carried by the process through time. By conditioning the process on its filtration, we introduce a family of higher order kernel mean embeddings (KMEs) that generalizes the notion of KME to capture additional information related to the filtration. We derive empirical estimators for the associated…

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