Probability Models For Computer Science by Sheldon M. Ross

Probability Models For Computer Science

bySheldon M. RossEditorSheldon M. Ross

Hardcover | June 11, 2001

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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

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...
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Details & Specs

Title:Probability Models For Computer ScienceFormat:HardcoverDimensions:304 pages, 9 × 6 × 0.98 inPublished:June 11, 2001Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0125980515

ISBN - 13:9780125980517

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Table of Contents

Review of Probability; Some Examples; Poisson and Compound Poisson Variables; Approximations and Processes; Markov Chains; Queuing; Random Algorithms and the Probabilistic Method; Martingales; Simulation.