Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Numerical Solution of SDE Through Computer Experiments (Universitext)

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Peter Eris Kloeden 作者
Springer
譯者
1993-12-20 出版日期
295 頁數
USD 79.95 價格
Paperback
universitext 叢書系列
9783540570745 圖書編碼

Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 圖書標籤: SDE  計算機科學  數學  of  numercial,  Through  Springer  Solution   


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Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 著者簡介


Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 著者簡介


Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Numerical Solution of SDE Through Computer Experiments (Universitext) 在線電子書 圖書描述

The book provides an easily accessible computationally oriented introduction into the numerical solution of stochastic differential equations using computer experiments. It develops in the reader an ability to apply numerical methods solving stochastic differential equations in their own fields. Furthermore, it creates an intuitive understanding of the necessary theoretical background from stochastic and numeric analysis. A downloadable softward containing programs for over 100 problems is provided at each of the following homepages:

http://www.math.uni-frankfurt.de/~numerik/kloeden/

http://www.business.uts.edu.au/finance/staff/eckhard.html

http.//www.math.siu.edu/schurz/SOFTWARE/

to enable the reader to develop an intuitive understanding of the issues involved. Applications include stochastic dynamical systems, filtering, parametric estimation and finance modeling.

The book is intended for readers without specialist stochastic background who want to apply such numerical methods to stochastic differential equations that arise in their own filed.

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