Introduction to Probability Models / Edition 9

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Overview

This best-selling reference is well-suited to those seeking to apply probability theory to phenomena in such fields as engineering, actuarial and management sciences, the physical and social sciences, and operations research.

Realistic models of real-world phenomena must take into account the possibility of randomness. More often than not, quantities are not predictable, but exhibit variations that should be taken into account by the model. This is usually accomplished by allowing the model to be probabilistic in nature. Such a model is referred as a probability model.

Introduction to Probability Models is a fascinating introduction to applications from diverse disciplines and an excellent introduction to a wide variety of applied probability topics.

* Best-selling book by a well-known author, with over 20,000 in sales for 7th edition

* Includes new examples and exercises in actuarial sciences

* Contains compulsory material for Exam 3 of the Society of Actuaries

Author Biography: Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He received his Ph.D. in statistics at Stanford University in 1968 and has been at Berkeley ever since. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Fourth Edition published by MacMillan, Introduction to Probability Models, Fifth Edition published by Academic Press, Stochastic Processes, Second Edition published by Wiley, and a new text, Introductory Statistics published by McGraw Hill.

Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences published by Cambridge University Press. He is a Fellow of the Institute of Mathematical Statistics, and a recipient of the Humboldt US Senior Scientist Award.

"...introduces elementary probability theory & stochastic processes...shows applications for engineering, the physical & social sciences, and operations research...features new material on K-records values & Ignatov's theorem.

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Editorial Reviews

From the Publisher
Praise from Reviewers:
“This is a fascinating introduction to applications from a variety of disciplines. Any curious student will love this book."
- Jean LeMaire, University of Pennsylvania
“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, Cal Polytechnic Institute
“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
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Product Details

  • ISBN-13: 9780125980623
  • Publisher: Elsevier Science
  • Publication date: 11/21/2006
  • Edition description: Older Edition
  • Edition number: 9
  • Pages: 800
  • Product dimensions: 6.36 (w) x 9.20 (h) x 1.23 (d)

Meet the Author

Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, and a recipient of the Humboldt US Senior Scientist Award.

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Table of Contents

Preface to the Fifth Edition
Preface to the Sixth Edition
Preface to the Seventh Edition
1 Introduction to Probability Theory 1
2 Random Variables 23
3 Conditional Probability and Conditional Expectation 93
4 Markov Chains 163
5 The Exponential Distribution and the Poisson Process 241
6 Continuous-Time Markov Chains 313
7 Renewal Theory and Its Applications 363
8 Queueing Theory 427
9 Reliability Theory 499
10 Brownian Motion and Stationary Processes 549
11 Simulation 585
Appendix: Solutions to Starred Exercises 649
Index 687
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Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted May 2, 2009

    Good supplement for the Stochastic Processes course that I took. Multiple examples, and solutions to starred problems was helpful.

    A bit more detail on some of the solutions for the starred exercises would have helped.

    Was this review helpful? Yes  No   Report this review
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