隨機(jī)微分方程:動(dòng)態(tài)系統(tǒng)方法(英文)
定 價(jià):88 元
叢書名:國(guó)外優(yōu)秀數(shù)學(xué)著作原版系列
- 作者: [美] 布蘭·霍林斯沃斯(Blane Hollingsworth) 著
- 出版時(shí)間:2021/7/1
- ISBN:9787560395135
- 出 版 社:哈爾濱工業(yè)大學(xué)出版社
- 中圖法分類:O211.63
- 頁(yè)碼:131
- 紙張:膠版紙
- 版次:1
- 開本:32開
《隨機(jī)微分方程:動(dòng)態(tài)系統(tǒng)方法(英文)》是一部英文版的數(shù)學(xué)專著,中文書名可譯為《隨機(jī)微分方程:動(dòng)態(tài)系統(tǒng)方法》。
《隨機(jī)微分方程:動(dòng)態(tài)系統(tǒng)方法(英文)》的作者是:布蘭·霍林斯沃斯(Blane Hollingsworth)教授,他于2008年獲得美國(guó)奧本大學(xué)博士學(xué)位。
談到隨機(jī)微分方程,不能不提到一位日本數(shù)學(xué)家,他就是伊藤清(ItoKiyosi,1915-2008),他精于概率論與函數(shù)解析理論,著有《隨機(jī)過(guò)程論》(1942)、《概率論基礎(chǔ)》(1944)、《論隨機(jī)微分方程》(1953)、《平穩(wěn)隨機(jī)分布》(1954)、《迷向隨機(jī)流》(1956)、《隨機(jī)過(guò)程》(1957)、《論隨機(jī)過(guò)程》(1960)、《擴(kuò)散過(guò)程及樣本路徑》(1965)等。他是許多大獎(jiǎng)的得主,而且很長(zhǎng)壽,
在中國(guó)隨機(jī)微分方程成為顯學(xué)是緣于彭實(shí)戈院士的成功,他創(chuàng)造性的研究了倒向隨機(jī)微分方程,并成功的將其應(yīng)用于金融資產(chǎn)定價(jià)問(wèn)題中,所以是一個(gè)既有學(xué)術(shù)深度又有廣闊“錢景”的好方向,彭院士也獲得了幾項(xiàng)大獎(jiǎng)。
數(shù)學(xué)知識(shí)每天都在增長(zhǎng),新的發(fā)現(xiàn)和大量的新信息使撰寫全面而翔實(shí)的著作變得越來(lái)越困難。
《隨機(jī)微分方程:動(dòng)態(tài)系統(tǒng)方法(英文)》是為了解決隨機(jī)微分方程(SDE)的基本問(wèn)題而寫,諸如“什么是隨機(jī)微分方程”。事實(shí)證明,回答此類基本問(wèn)題也需要非常有深度的背景知識(shí)。
Mathematics knowledge grows every day; new discoveries and overwhelming amounts of new information make it more and more difficult to write comprehensive yet informative texts. This one developed as an attempt to pin down the basics of stochastic differential equations (SDE's), simple questions like, \"What is a stochastic differential equation?\" It turns out the depth behind the requisite knowledge to answer such an elementary question is quite substantial.
Many great texts already exist that attack SDE's from the stochastic perspective, but our main objective is to present the material from the dynamical systems perspective, aimed at the audience familiar with classical analysis and differential equations. Really, my advisor Paul Schmidt at Auburn University had the idea of presenting the material from a \"new cultural perspective\" and his contribution to this work is enormous. We feel that this presentation will help mathematicians understand, with a minimum of technicality, what SDE's are, and if they are appropriate for their particular modeling/applications.
1 INTRODUCTION AND PRELIMINARIES
1.1 Stochastic Processes and Their Distributions
1.2 Semigroups of Linear Operators
1.3 Kernels and Semigroups of Kernels
1.4 Conditional Expectation, Martingales, and Markov Processes
1.5 Brownian Motion
2 ITO INTEGRALS AND STOCHASTIC DIFFERENTIAL EQUATIONS
2.1 The Ito Integral
2.2 Stochastic Differential Equations and their Solutions
2.3 Ito's Formula and Examples
3 DYNAMICAL SYSTEMS AND STOCHASTIC STABILITY
3.1 \"Stochastic Dynamical Systems\"
3.2 Koopman and Frobenius-Perron Operators: The Deterministic Case
3.3 Koopman and Frobenius-Perron Operators: The Stochastic Case
3.4 Liapunov Stability
3.5 Markov Semigroup Stability
3.6 Long-time behavior of a stochastic predator-prey model
BIBLIOGRAPHY
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