Probability Models for Computer Science [Hardcover]

Sheldon M. Ross (Author)

Book Description

Publication Date: June 25, 2001 | ISBN-10: 0125980515 | ISBN-13: 978-0125980517 | Edition: 1

The role of probability in computer science has been growing for years and, in lieu of a tailored textbook, many courses have employed a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer science graduate student (and the advanced undergraduate), best-selling author Sheldon Ross has developed the premier probability text for aspiring computer scientists involved in computer simulation and modeling. The math is precise and easily understood. As with his other texts, Sheldon Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science and related majors and practitioners.

Many interesting examples and exercises have been chosen to illuminate the techniques presented

Examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organising list problems, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queuing networks, distributed workload models, and many othersMany interesting examples and exercises have been chosen to illuminate the techniques presented

Editorial Reviews

From the Back Cover

The role of probability in computer science is growing and, in lieu of a tailored book, many professionals employ a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer scientists, probability and statistics sage Sheldon Ross has developed the premier probability title for computer scientists involved in computer simulation and modeling.

A clear understanding of the nature of probability modeling is an essential task in developing computer systems and software.

The math is precise and easily understood. As with his other best-selling titles, Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science (and related) topics.

A key feature of this book is its many interesting examples and exercises that have been chosen to illuminate the techniques presented. For instance, there are examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organizing list problems, antichains, minimal and maximal cuts in graphs, random permutations, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queing networks, distributed workload models, and more.

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.

Product Details

• Hardcover: 304 pages
• Publisher: Academic Press; 1 edition (June 25, 2001)
• Language: English
• ISBN-10: 0125980515
• ISBN-13: 978-0125980517
• Product Dimensions: 9 x 6 x 0.9 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #1,147,239 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

5.0 out of 5 stars great book, use it in Probabilistic methods course, June 9, 2008

By 

I. Kleiner - See all my reviews
(REAL NAME)   

This review is from: Probability Models for Computer Science (Hardcover)

This is great book for Computer Science students which studies Probabilistic Methods course. The book is selfcontained. Well explained. Has a lot of interesting and complecated examples.

Martingales, using of tail inequalities, many other tehniques covered in this book.

I taught according to this book and highly recommendet it.

1 of 2 people found the following review helpful:

3.0 out of 5 stars Lots of theory; no applications, February 21, 2004

By A Customer

Amazon Verified Purchase

This review is from: Probability Models for Computer Science (Hardcover)

I was very disappointed by this book. While it does an excellent job of presenting and analyzing the THEORY of various kinds of probability models, it says almost nothing about how to apply these models to the problems of computer science. For example queueing theory is incredibly useful in many areas of performance modeling, but Ross doesn't mention any of them. Instead, he presents queueing theory as if it fell from the sky one day -- pristine and beautiful, not to be tarnished by having any actual purpose. The presentation reminds me of the way some "pure" mathematicians take offense when a physicist even hints that their beautiful equations might be used to solve an actual problem. If you're interested in applying this stuff, take a look instead at Trivedi's "Probability and Statistics with Reliability, Queueing, and Computer Science Applications, 2nd Edition." It's a much better book.