Probability Models for Computer Science
By (author) Sheldon M. Ross
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Probability Models for Computer Science by Sheldon M. Ross
Book DescriptionThe 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 others. Many interesting examples and exercises have been chosen to illuminate the techniques presented.
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Book DetailsISBN: 9780125980517
(229mm x 152mm x 18mm)
Imprint: Academic Press Inc
Publisher: Elsevier Science Publishing Co Inc
Publish Date: 12-Jul-2001
Country of Publication: United States
Books By Author Sheldon M. Ross
Introduction to Probability Models, Hardback (March 2014)
Suitable for a first undergraduate course in applied probability, this book introduces elementary probability theory and stochastic processes, and shows how probability theory can be applied fields such as engineering, computer science, management science, the physical and social sciences, and operations research.
Simulation, Hardback (December 2012)» View all books by Sheldon M. Ross
Introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. This title presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.
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Author Biography - Sheldon M. Ross
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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