---

＜電子ブック＞
Proceedings of the Forum "Math-for-Industry" 2018 : Big Data Analysis, AI, Fintech, Math in Finances and Economics / edited by Jin Cheng, Xu Dinghua, Osamu Saeki, Tomoyuki Shirai
(Mathematics for Industry. ISSN:21983518 ; 35)

版	1st ed. 2021.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XIV, 179 p. 63 illus., 50 illus. in color : online resource
著者標目	Cheng, Jin editor Dinghua, Xu editor Saeki, Osamu editor Shirai, Tomoyuki editor SpringerLink (Online service)
件　名	LCSH:Engineering mathematics LCSH:Quantitative research LCSH:Statistics  FREE:Engineering Mathematics FREE:Data Analysis and Big Data FREE:Applied Statistics
一般注記	A Brief Review of Some Swarming Models using Stochastic Differential Equations -- Copula-based estimation of Value at Risk for the portfolio problem -- An Overview of Exact Solution Methods for Guaranteed Minimum Death Benefit Options in Variable Annuities -- Determinantal reinforcement learning with techniques to avoid poor local optima -- Surface Denoising based on Normal Filtering in a Robust Statistics Framework -- Mathematical Modeling and Inverse Problem Approaches for Functional -- Clothing Design based on Thermal Mechanism -- Unique continuation on a sphere for Helmholtz equation and its numerical treatments -- Notes on Backward Stochastic Differential Equations for Computing XVA This volume includes selected technical papers presented at the Forum “Math-for-Industry” 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors HTTP:URL=https://doi.org/10.1007/978-981-16-5576-0

 類似資料