An Introduction to Stochastic Modeling book cover

An Introduction to Stochastic Modeling

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, 4e, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems.

 

New to this edition:

  • Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications
  • Plentiful, completely updated problems
  • Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers
  • New chapters of stochastic differential equations and Brownian motion and related processes
  • Additional sections on Martingale and Poisson process

Paperback, 584 Pages

Published: December 2010

Imprint: Academic Press

ISBN: 978-0-12-381416-6

Reviews

  • PRAISE FOR THE SECOND EDITION
    This book is a valuable resource for anyone studying combustion processes."
    --David L. Liscinsky, United Technologist Research Center, in AIAA JOURNAL

    This is an excellent text-book ... The narrative is clear, careful and detailed but, at the same time, designed to draw (not to bore) the reader in. The main strengths, in my opinion, are the wealth of convincing applications, which are discussed at some, but not too much length after each bit of theoretical development, and the large number of exercises given at the ends of sections, not just at the ends of chapters."
    --Martin Crowder, University of Surrey, Guildford, in THE STATISTICIAN


Contents

  • Introduction
    Conditional Probability and Conditional Expectation
    Markov Chains: Introduction
    The Long Run Behavior of Markov Chains
    Poisson Processes
    Continuous Time Markov Chains
    Renewal Phenomena
    Brownian Motion and Related Processes
    Queueing Systems
    Random Evolutions
    Characteristic Functions and Their Applications

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