Introduction to Probability Models book cover

Introduction to Probability Models

Sheldon Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It introduces elementary probability theory and stochastic processes, and shows how probability theory can be applied fields such as engineering, computer science, management science, the physical and social sciences, and operations research.

The hallmark features of this renowned text remain in this eleventh edition: superior writing style; excellent exercises and examples covering the wide breadth of coverage of probability topic; and real-world applications in engineering, science, business and economics. The 65% new chapter material includes coverage of finite capacity queues, insurance risk models, and Markov chains, as well as updated data.

Professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.

Hardbound, 784 Pages

Published: January 2014

Imprint: Academic Press

ISBN: 978-0-12-407948-9


  • Praise from Reviewers for the 10th edition:

    "I think Ross has done an admirable job of covering the breadth of applied probability. Ross writes fantastic problems which really force the students to think divergently...The examples, like the exercises are great."--Matt Carlton, California Polytechnic Institute

    "This is a fascinating introduction to applications from a variety of disciplines. Any curious student will love this book."--Jean LeMaire, University of Pennsylvania

    "This book may be a model in the organization of the education process. I would definitely rate this text to be the best probability models book at its level of difficulty...far more sophisticated and deliberate than its competitors."--Kris Ostaszewski, University of Illinois


  • Preface

    1. Introduction to Probability Theory
    2. Random Variables
    3. Conditional Probability and Conditional Expectation
    4. Markov Chains
    5. The Exponential Distribution and the Poisson Process
    6. Continuous-Time Markov Chains
    7. Renewal Theory and Its Applications
    8. Queueing Theory
    9. Reliability Theory
    10. Brownian Motion and Stationary Processes
    11. Simulation

    Appendix: Solutions to Starred Exercises


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