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ORCIDToshihiro Yamada
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2020 – today
- 2024
[j20]Masaya Kannari, Riu Naito, Toshihiro Yamada:
Asymptotic Expansion and Weak Approximation for a Stochastic Control Problem on Path Space. Entropy 26(2): 119 (2024)- [j19]Naho Akiyama, Toshihiro Yamada:
A weak approximation for Bismut's formula: An algorithmic differentiation method. Math. Comput. Simul. 216: 386-396 (2024) - [i5]Yuga Iguchi, Toshihiro Yamada:
An extended Milstein scheme for effective weak approximation of diffusions. CoRR abs/2409.00524 (2024) - [i4]Reiichiro Kawai, Riu Naito, Toshihiro Yamada:
A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps. CoRR abs/2410.09666 (2024) - 2023
- [j18]Toshihiro Yamada:
Total variation bound for Milstein scheme without iterated integrals. Monte Carlo Methods Appl. 29(3): 221-242 (2023) - [j17]Naho Akiyama, Toshihiro Yamada:
Discrete Bismut formula: Conditional integration by parts and a representation for delta hedging process. Risk Decis. Anal. 9(1): 1-9 (2023) - [i3]Akihiko Takahashi, Toshihiro Yamada:
New asymptotic expansion formula via Malliavin calculus and its application to rough differential equation driven by fractional Brownian motion. CoRR abs/2306.13405 (2023) - 2022
- [j16]Yuga Iguchi, Toshihiro Yamada:
Weak approximation of SDEs for tempered distributions and applications. Adv. Comput. Math. 48(5): 52 (2022) - [j15]Akihiko Takahashi, Yoshifumi Tsuchida
, Toshihiro Yamada:
A new efficient approximation scheme for solving high-dimensional semilinear PDEs: Control variate method for Deep BSDE solver. J. Comput. Phys. 454: 110956 (2022) - [j14]Naho Akiyama, Toshihiro Yamada:
A high order weak approximation for jump-diffusions using Malliavin calculus and operator splitting. Monte Carlo Methods Appl. 28(2): 97-110 (2022) - [j13]Toshihiro Yamada:
Short Communication: A Gaussian Kusuoka Approximation without Solving Random ODEs. SIAM J. Financial Math. 13(1): 1- (2022) - [c1]Riu Naito, Toshihiro Yamada:
A deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to CVA computation. CIFEr 2022: 1-8 - 2021
- [j12]Toshihiro Yamada:
High order weak approximation for irregular functionals of time-inhomogeneous SDEs. Monte Carlo Methods Appl. 27(2): 117-136 (2021) - [j11]Kimiki Tokutome, Toshihiro Yamada:
Acceleration of automatic differentiation of solutions to parabolic partial differential equations: a higher order discretization. Numer. Algorithms 86(2): 593-635 (2021) - [i2]Akihiko Takahashi, Yoshifumi Tsuchida, Toshihiro Yamada:
A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for Deep BSDE solver. CoRR abs/2101.09890 (2021) - 2020
- [i1]Riu Naito, Toshihiro Yamada:
A machine learning solver for high-dimensional integrals: Solving Kolmogorov PDEs by stochastic weighted minimization and stochastic gradient descent through a high-order weak approximation scheme of SDEs with Malliavin weights. CoRR abs/2012.12346 (2020)
2010 – 2019
- 2019
- [j10]Toshihiro Yamada, Kenta Yamamoto:
Second Order Discretization of Bismut-Elworthy-Li Formula: Application to Sensitivity Analysis. SIAM/ASA J. Uncertain. Quantification 7(1): 143-173 (2019) - [j9]Riu Naito, Toshihiro Yamada:
A third-order weak approximation of multidimensional Itô stochastic differential equations. Monte Carlo Methods Appl. 25(2): 97-120 (2019) - [j8]Yusuke Okano, Toshihiro Yamada:
A control variate method for weak approximation of SDEs via discretization of numerical error of asymptotic expansion. Monte Carlo Methods Appl. 25(3): 239-252 (2019) - [j7]Riu Naito, Toshihiro Yamada:
A second-order discretization for forward-backward SDEs using local approximations with Malliavin calculus. Monte Carlo Methods Appl. 25(4): 341-361 (2019) - [j6]Toshihiro Yamada:
An Arbitrary High Order Weak Approximation of SDE and Malliavin Monte Carlo: Analysis of Probability Distribution Functions. SIAM J. Numer. Anal. 57(2): 563-591 (2019) - 2018
- [j5]Toshihiro Yamada, Kenta Yamamoto:
A second-order weak approximation of SDEs using a Markov chain without Lévy area simulation. Monte Carlo Methods Appl. 24(4): 289-308 (2018) - 2017
- [j4]Toshihiro Yamada:
A higher order weak approximation scheme of multidimensional stochastic differential equations using Malliavin weights. J. Comput. Appl. Math. 321: 427-447 (2017) - 2015
- [j3]Akihiko Takahashi, Toshihiro Yamada:
On Error Estimates for Asymptotic Expansions with Malliavin Weights: Application to Stochastic Volatility Model. Math. Oper. Res. 40(3): 513-541 (2015) - 2013
- [j2]Takashi Kato, Akihiko Takahashi, Toshihiro Yamada:
An asymptotic expansion formula for up-and-out barrier option price under stochastic volatility model. JSIAM Lett. 5: 17-20 (2013) - 2012
- [j1]Akihiko Takahashi, Toshihiro Yamada:
An Asymptotic Expansion with Push-Down of Malliavin Weights. SIAM J. Financial Math. 3(1): 95-136 (2012)
Coauthor Index
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