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An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Toyomu Matsuda

We give an extension of Le's stochastic sewing lemma. The stochastic sewing lemma proves convergence in

L_m

of Riemann type sums

\sum _{[s,t] \in \pi } A_{s,t}

for an adapted two-parameter stochastic process A, under certain conditions on the moments o ...

Cambridge Univ Press2024

Espérance conditionnelle

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An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Geometrically-Conditioned Point Diffusion Models

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

A Wasserstein-based measure of conditional dependence

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Differential Entropy of the Conditional Expectation under Gaussian Noise

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

High resolution density pedestal measurements during edge localized modes by short-pulse reflectometry in the TCV tokamak

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

A Wasserstein-based measure of conditional dependence

Differential Entropy of the Conditional Expectation under Gaussian Noise

An extension of the stochastic sewing lemma and applications to fractional stochastic calculus

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Geometrically-Conditioned Point Diffusion Models

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

High resolution density pedestal measurements during edge localized modes by short-pulse reflectometry in the TCV tokamak

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR