An Introduction to Stochastic Modeling / Edition 4

An Introduction to Stochastic Modeling / Edition 4

ISBN-10:
0123814162
ISBN-13:
9780123814166
Pub. Date:
12/10/2010
Publisher:
Elsevier Science
ISBN-10:
0123814162
ISBN-13:
9780123814166
Pub. Date:
12/10/2010
Publisher:
Elsevier Science
An Introduction to Stochastic Modeling / Edition 4

An Introduction to Stochastic Modeling / Edition 4

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Overview

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, Fourth Edition, 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

Product Details

ISBN-13: 9780123814166
Publisher: Elsevier Science
Publication date: 12/10/2010
Edition description: Fourth Edition
Pages: 584
Product dimensions: 6.00(w) x 9.10(h) x 1.40(d)

Table of Contents

IntroductionConditional Probability and Conditional ExpectationMarkov Chains: IntroductionThe Long Run Behavior of Markov ChainsPoisson ProcessesContinuous Time Markov ChainsRenewal PhenomenaBrownian Motion and Related ProcessesQueueing Systems Random EvolutionsCharacteristic Functions and Their Applications

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This classic bestselling text serves as the foundation for a one-semester course in stochastic processing for students familiar with elementary probability theory and calculus

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