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Introduction to Probability Models Hardcover – 11 Dec 2009
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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 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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Top Customer Reviews
I just wish the teacher's solution manual were available to anyone (specially if you are doing like me self study).
However I would recommend to anyone not exposed previously to probability, to get acquainted first with the subject by studying and solving all the problems from the excellent book of Probability (Modular Mathematics Series) by John McColl
Most Helpful Customer Reviews on Amazon.com (beta)
In my opinion, this text provides a strong foundation. It makes books like All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics), easier to use and harder to abuse.
- Clarity: There are no missing steps in the math, you don't have to doodle in the margins to derive the next equation.
- Mathematical ease: This is calculus-based probability but the calculus is not difficult and the algebra is crystal clear.
- Completeness: The book clearly presents core concepts concisely; it is not telegraphic. You will be introduced to probability distributions, conditional probability, Markov models, queuing theory, stochastic processing and methods for simulating distributions.
- Theory: The text mentions or proves relevant theorems at the rate of 1 every 10 pages. The proofs were simple and the theorems are crucial - for introductory texts, that counts as theory for me.
- Examples: There are many good examples that make it more memorable.
- Structure: Overall, I thought the structure was good. As a novice, I particularly liked the 2nd chapter on random variables - a clean approach to various probability distributions, their parameters and functions. As a scientist, I was grateful for the clear introduction to queues and stochastic processes.
Other reviewers have complained about structure and, since this doesn't really make sense, I am guessing that it comes from failed expectations. This is not a introduction to statistics. You won't find the Wald test, Bayesian testing, Pearson's chi-squared, a likelihood ratio test or any other workaday statistical test. If that is what you want, then go to "All of Statistics" (see above).
- Graphics (or lack thereof): I could have used more figures. Out of curiosity, I wrote a program to see probability density functions for different parameters and variables. Markov chains without actual transition diagrams seemed odd.
- Odd-man-out chapters: I didn't really understand the inclusion of the Reliability chapter (probably just me).
- Redundant/simple examples: For example, the Renewal chapter had near identical examples(in one, lightbulbs blown out, in another, batteries depleted) but nothing more compelling - what about evolution?? Mutation is a Poisson process, but sometimes the mutation creates a selective sweep which is a renewal process, so what kind of process is evolution? Horse kicks are Poisson but if you replace the horse every time it kicks someone, then that's renewal. So what's the likelihood of a horse kick with renewal? I would have enjoyed some more complicated examples.
Nitpicking aside, I thought this was a great book.
As for this book itself, the first two chapters are pretty quick, cut, and dry. They cover the material necessary and adequately for later sections with no real B.S., but sometimes it can feel a little too dry (which is why I recommend a stronger background first).
Chapter 3 is perhaps the most important chapter in the book. I can not stress enough the importance of mastering the concepts in chapter 3 - conditioning is by far the most important problem solving technique in this book.
Tons of good exercises ranging from easy to very hard, and starred exercises have detailed solutions in the back.
The homework problems help with the understanding of the material, but the text itself does a poor job of conveying the information in a coherent manner (I guess you have to buy the solutions manual if you want to learn the material). The book complicates simple concepts and does not organize itself in a way that lends itself to self-learning. I had lecture notes from the professor, which clarified the material greatly. Beyond the 5-10 hours I struggled reading the book at the beginning of the semester, I only touched the book when I sold it to a friend.
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