Essentials of Stochastic Processes

By: Richard Durrett

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Paperback | 22 April 2018 | Edition Number 3

At a Glance

Paperback
288 Pages

Edition Type
Revised

Dimensions(cm)
23.5 x 15.49 x 2.0

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Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader''s understanding.

Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatment of other topics useful for applications has been expanded. In addition, the ordering of topics has been improved; for example, the difficult subject of martingales is delayed until its usefulness can be applied in the treatment of mathematical finance.

Industry Reviews

"It is the 3rd edition of the textbook devoted to initial information and basic topics from the theory of stochastic processes. ... The book is very useful for anyone who is interested in probability theory and its ramifications and applications. It can be recommended both for students and postgraduates, teachers and practitioners. ... The book contains a lot of examples which contribute to a better understanding of the text." (Yuliya S. Mishura, zbMATH 1378.60001, 2018)

"This is the third edition of a popular textbook on stochastic processes. It is intended for advanced undergraduates and beginning graduate students and aimed at an intermediate level between an undergraduate course in probability and the first graduate course that uses measure theory." (William J. Satzer, MAA Reviews, maa.org, February, 2017)

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