Introduction to stochastic processes
Author(s)
Bibliographic Information
Introduction to stochastic processes
Chapman & Hall/CRC, 2006
2nd ed
Available at 27 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
LAW||9||4 ||複本06031969
Note
Includes index
Description and Table of Contents
Description
Emphasizing fundamental mathematical ideas rather than proofs, Introduction to Stochastic Processes, Second Edition provides quick access to important foundations of probability theory applicable to problems in many fields. Assuming that you have a reasonable level of computer literacy, the ability to write simple programs, and the access to software for linear algebra computations, the author approaches the problems and theorems with a focus on stochastic processes evolving with time, rather than a particular emphasis on measure theory.
For those lacking in exposure to linear differential and difference equations, the author begins with a brief introduction to these concepts. He proceeds to discuss Markov chains, optimal stopping, martingales, and Brownian motion. The book concludes with a chapter on stochastic integration. The author supplies many basic, general examples and provides exercises at the end of each chapter.
New to the Second Edition:
Expanded chapter on stochastic integration that introduces modern mathematical finance
Introduction of Girsanov transformation and the Feynman-Kac formula
Expanded discussion of Ito's formula and the Black-Scholes formula for pricing options
New topics such as Doob's maximal inequality and a discussion on self similarity in the chapter on Brownian motion
Applicable to the fields of mathematics, statistics, and engineering as well as computer science, economics, business, biological science, psychology, and engineering, this concise introduction is an excellent resource both for students and professionals.
Table of Contents
Preliminaries. Finite Markov Chains. Countable Markov Chains. Continuous-Time Markov Chains. Optimal Stopping. Martingales. Renewal Processes. Reversible Markov Chians. Brownian Motion. Stochastic Integration.
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