Introduction to Stochastic Processes, Second Edition

Gregory F. Lawler

May 16, 2006 by Chapman and Hall/CRC
Textbook - 248 Pages - 13 B/W Illustrations
ISBN 9781584886518 - CAT# C651X
Series: Chapman & Hall/CRC Probability Series


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  • Emphasizes the relationship between convergence to equilibrium and the size of the eigenvalues of the stochastic matrix
  • Discusses the Poisson process, finite state space, and birth-and-death processes using forward differential equations to describe the evolution of the probabilities
  • Supplies a solid introduction to martingales that includes a discussion of optional sampling and the martingale convergence theorem and their proofs
  • Includes current topics in the realm of reversible Markov chains and introduces Markov chain algorithms important to some areas of physics, computer science, and statistics
  • Presents an introduction to Brownian motion, both multidimensional and one-dimensional
  • Introduces stochastic integration with application to mathematical finance
  • Summary

    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 Itô'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.
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