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Asymptotic Expansion and Weak Approximation

Applications of Malliavin Calculus and Deep Learning

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Éditeur :

Springer


Paru le : 2025-10-02



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Description

This book provides a self-contained lecture on a Malliavin calculus approach to asymptotic expansion and weak approximation of stochastic differential equations (SDEs),  along with numerical methods for computing parabolic partial differential equations (PDEs).
Constructions of weak approximation and asymptotic expansion are given in detail using Malliavin’s integration by parts with theoretical convergence analysis.
Weak approximation algorithms and Python codes are available with numerical examples.
Moreover, the weak approximation scheme is effectively applied to high-dimensional nonlinear problems without suffering from the curse of dimensionality
through combining with a deep learning method.
Readers including graduate-level students, researchers, and practitioners can understand both theoretical and applied aspects of recent developments of asymptotic expansion and weak approximation.
Pages
97 pages
Collection
n.c
Parution
2025-10-02
Marque
Springer
EAN papier
9789819682799
EAN PDF
9789819682805
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
1444 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
47,46 €
EAN EPUB
9789819682805
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
9
Taille du fichier
8714 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
47,46 €

Akihiko Takahashi is at Graduate School of Economics, The University of Tokyo

Toshihiro Yamada is at Graduate School of Economics, Hitotsubashi University

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