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

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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

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

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Chattez avec Graph Search

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

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

Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Minimax rate for optimal transport regression between distributions

fGOT: Graph Distances Based on Filters and Optimal Transport

A Wasserstein-based measure of conditional dependence

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

Minimax rate for optimal transport regression between distributions

Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

A Wasserstein-based measure of conditional dependence

fGOT: Graph Distances Based on Filters and Optimal Transport

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method