Stochastic Processes / Edition 2

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Overview

A nonmeasure theoretic introduction to stochastic processes. Considers its diverse range of applications and provides readers with probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments; a new chapter on Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs. Numerous exercises and problems have been added throughout the text.
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Editorial Reviews

Booknews
A nonmeasure theoretical introduction to stochastic processes for students with a knowledge of calculus and elementary probability. Uses a probabilistic rather than an analytic approach whenever possible, for example describing most processes from a sample path perspective. The second edition includes new chapters on martingales and Poisson random variables, adds more information on a number of topics, and rearranges the order of the discussion. The date of the first edition is not indicated. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780471120629
  • Publisher: Wiley
  • Publication date: 2/28/1995
  • Series: Wiley Series in Probability and Statistics
  • Edition description: REV
  • Edition number: 2
  • Pages: 528
  • Product dimensions: 6.48 (w) x 9.39 (h) x 1.17 (d)

Table of Contents

Preliminaries.

The Poisson Process.

Renewal Theory.

Markov Chains.

Continuous-Time Markov Chains.

Martingales.

Random Walks.

Brownian Motion and Other Markov Processes.

Stochastic Order Relations.

Poisson Approximations.

Answers and Solutions to Selected Problems.

Index.

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  • Anonymous

    Posted May 2, 2009

    I did not find it the best for an introductory Stochastic Processes Course. It may be much better for an intermediate course.

    I did not find the book to have the same clarity of examples and explanation as I did with Sheldon Ross' "A First Course in Probability," - which I thouroughly enjoyed and gave a high rating.

    During the course that I took, I found myself referencing Ross' Probability Models book (better for an introductory course), and Taylor and Karlin's, "An introduction to Stochastic Modeling." Taylor and Karlin's book was the best by a good margin, I would highly recommend it and I used it almost exclusively towards the end of the course that I took.

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