Introduction to Probability Models by Sheldon M. Ross

Product Description

The seventh edition of the successful Introduction to Probability Models introduces elementary probability theory and the stochastic processes and is particularly well-suited to those applying probability theory to the study of phenomena in engineering, management science, the physical and social sciences, and operations research. Skillfully organized, Introduction to Probability Models covers all essential topics. Sheldon Ross, a talented and prolific textbook author, distinguishes this carefully and substantially revised book by his effort to develop in students an intuitive, and therefore lasting, grasp of probability theory. The seventh edition includes many new examples and exercises, with the majority of the new exercises being less demanding of the student. In addition, the text introduces stochastic processes, stressing applications, in an easily understood manner. There is a comprehensive introduction to the applied models of probability that stresses intuition. Both students and professors will agree that this is the most solid and widely used text for probability theory.

* Provides a detailed coverage of the Markov Chain Monte Carlo methods and Markov Chain covertimes
* Gives a thorough presentation of k-record values and the surprising Ignatov's theorem
* Includes examples relating to: "Random walks to circles," "The matching rounds problem," "The best prize problem" and many more
* Contains a comprehensive appendix with the answers to approximately 100 exercises from throughout the text
* Accompanied by a complete instructor's solutions manual with step-by-step solutions to all exercises
NEW TO THIS EDITION
* Includes many new and easier examples and exercises
* Offers new material on utilizing probabilistic method in combinatorial optimization problems
* Includes new material on suspended animation reliability models
* Contains new material on random algorithms and cycles of random permutations
Product Details

* Amazon Sales Rank: #834330 in Books
* Published on: 2000-02
* Number of items: 1
* Binding: Hardcover
* 693 pages

Editorial Reviews

Review
“...perfect for actuaries…a fascinating introduction to applications from a variety of disciplines. Any curious student will love this book."
--Jean Lemaire, University of Pennsylvania, Wharton School

“The examples, like the exercises are great...”
--Matt Carlton, California Polytechnic State University

Book Info
Includes nearly 600 new or updated exercises, with over 100 solutions provided, new derivations for the Poisson and nonhomogeneous Poisson processes, optimization of single server, general service time queue and analysis of a series structure reliability model in which components enter a state of suspended animation upon cohort failure.

Back Cover Copy
The Seventh Edition of Ross' Intorduction to Probability Models represents the continuing convergence of this best-selling book with the widening indispensability of probability in pure and applied science.
Revised and updated, Introduction to Probability Models is particularly well suited to those seeking an understanding of how probability theory and stochastic processes apply to phenomena in such fields as engineering, management science, the physical and social sciences, and operations research.
While retaining its focus on elementary probability and stochastic processes, this edition's significant revisions include:
* Nearly 600 new or updated exercises, with over 100 solutions provided
* New derivations for the Poisson and nonhomogeneous Poisson processes
* Optimization of a single server, general service time queue
* Analysis of a series structure reliability model in which components enter a state of suspended animation upon cohort failure
Sheldon M. Ross has published numerous textbooks and technical articles in the areas of statistics and applied probability. 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 U.S. Senior Scientist Award.
Customer Reviews

does not explain the concepts so well; just one proposition after the other2
We had this book for a 4th year Computer Science - Statistics course.

I agree with some of the other reviewers that - inspite of claiming to be an 'introductory' text book - it does not explain the concepts so well.

e.g. Bayes Theorem has been introduced in like half a page with absolutely no explaination of prior and posterior probablities and the underlying concepts (something I learnt when we applied Bayes Formula in a Neural Networks & Data Mining course)

So all you get are the formulae from this book (at least in the first few chapters that I read), where the author should have spent more time 'introducing' concepts.

The solved examples are ok, but very academic - and there is no way to be sure of your answers for the other non-solved questions (unless you have a lecturer to discuss them with)

2 Stars - because they ought to start writing math books that regular people can read and understand and appreciate - not just math prodigies

Dense and difficult to follow.2
This book contains a wealth of information about probability models, but it's so hard to follow that I can't extract any of that information to make any use of it. From the other reviews, I gather that it is a good resource for some. But this definitely not an INTRODUCTION to Probability Models unless you have a very strong background in general probability.

one of the best introduction to probability and stochastic processes5
Understanding probability requires various resources to read. I think this book is one of the irreplaceable element in these resources. It is an introduction book as the name implies. Examples are illuminating the subject very well.