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Synopsis
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, and test bank
- 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
"synopsis" may belong to another edition of this title.
About the Author
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD 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, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
Review
Praise from Reviewers:
“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
"About this title" may belong to another edition of this title.
- PublisherAcademic Press
- Publication date2009
- ISBN 10 0123756863
- ISBN 13 9780123756862
- BindingHardcover
- LanguageEnglish
- Number of pages800
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