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 | | Probability Models for Computer Science by Sheldon M. Ross ISBN-10: 9780125980517 ISBN-10: 0-12-598051-5 ISBN-13: 9780125980517 ISBN-13: 978-0-12-598051-7 Hardcover 2001-07 Academic Press Find Lowest Price |
Editorials | ||
Product Description 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 | ||
Reviews | ||
great book, use it in Probabilistic methods course 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. | ||
Lots of theory; no applications 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. | ||