Introduction to Probability Models

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Academic Press, Dec 11, 2006 - Mathematics - 800 pages
3 Reviews
Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text.

The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students.
This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.

New to this Edition:

  • 65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains
  • Contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams
  • Updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, test bank, and companion website
  • Includes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field

Hallmark features:

  • Superior writing style
  • Excellent exercises and examples covering the wide breadth of coverage of probability topics
  • Real-world applications in engineering, science, business and economics
 

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User Review - Flag as inappropriate

This book is great. I had two classes with Sheldon Ross at USC. He is really smart and this book is very comprehensive. Complex topics are covered very clearly.

Review: Introduction to Probability Models

User Review  - Jette Stuart - Goodreads

Sheldon Ross is a genius of our time. This is an excellent book for introduction to stochastic processes, a subject that I am sure most find challenging. Read full review

Contents

Chapter 3 Conditional Probability and Conditional Expectation
97
Chapter 4 Markov Chains
191
Chapter 5 The Exponential Distribution and the Poisson Process
291
Chapter 6 ContinuousTime Markov Chains
371
Chapter 7 Renewal Theory and Its Applications
421
Chapter 8 Queueing Theory
497
Chapter 9 Reliability Theory
579
Chapter 10 Brownian Motion and Stationary Processes
631
Chapter 11 Simulation
667
Solutions to Starred Exercises
735
Index
775
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About the author (2006)

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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