A weak approximation for Bismut’s formula: An algorithmic differentiation method-Reference-Cited by-同舟云学术

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A weak approximation for Bismut’s formula: An algorithmic differentiation method

Published:2024-02 Issue: Volume:216 Page:386-396
ISSN:0378-4754
Container-title:Mathematics and Computers in Simulation
language:en
Short-container-title:Mathematics and Computers in Simulation

Author:

Akiyama Naho,Yamada Toshihiro

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Precursory Research for Embryonic Science and Technology

Publisher

Elsevier BV

Subject

Applied Mathematics,Modeling and Simulation,Numerical Analysis,General Computer Science,Theoretical Computer Science

Reference20 articles.

1. Automatic differentiation in machine learning: a survey;Baydin;J. Mach. Learn. Res.,2018

2. Large Deviations and the Malliavin Calculus;Bismut,1984

3. Monte Carlo Methods in Financial Engineering;Glasserman,2013

4. Sensitivity analysis using Itô-Malliavin calculus and martingales, and application to stochastic optimal control;Gobet;SIAM J. Control Optim.,2005

5. Efficient and practical implementations of cubature on Wiener space;Gyurkó,2010

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