Probability Models for Computer Science / Edition 1

Probability Models for Computer Science / Edition 1

by Sheldon M. Ross
     
 

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ISBN-10: 0125980515

ISBN-13: 9780125980517

Pub. Date: 06/25/2001

Publisher: Elsevier Science

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

Overview

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

Product Details

ISBN-13:
9780125980517
Publisher:
Elsevier Science
Publication date:
06/25/2001
Edition description:
New Edition
Pages:
304
Product dimensions:
0.81(w) x 6.00(h) x 9.00(d)

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.

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