An Introduction to Stochastic Modeling

✍ Scribed by Howard M. Taylor and Samuel Karlin (Auth.)

Publisher: Elsevier Inc, Academic Press
Year: 1984
Tongue: English
Leaves: 403
Category: Library

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✦ Synopsis

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, Third 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.
Realistic applications from a variety of disciplines integrated throughout the text
Plentiful, updated and more rigorous problems, including computer "challenges"
Revised end-of-chapter exercises sets-in all, 250 exercises with answers
New chapter on Brownian motion and related processes
Additional sections on Matingales and Poisson process
Solutions manual available to adopting instructors

✦ Table of Contents

Content:
Front Matter, Page iii
Copyright, Page iv
Preface, Pages ix-x
Chapter 1 - Introduction, Pages 1-43
Chapter 2 - Conditional Probability and Conditional Expectation, Pages 44-66
Chapter 3 - Markov Chains: Introduction, Pages 67-119
Chapter 4 - The Long Run Behavior of Markov Chains, Pages 120-172
Chapter 5 - Poisson Processes, Pages 173-209
Chapter 6 - Continuous Time Markov Chains, Pages 210-273
Chapter 7 - Renewal Phenomena, Pages 274-304
Chapter 8 - Branching Processes and Population Growth, Pages 305-340
Chapter 9 - Queueing Systems, Pages 341-386
Further Readings, Pages 387-388
Solutions to Selected Problems, Pages 389-394
Index, Pages 395-399

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