Probability Models For Computer Sciences

Key Features 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 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. Readership Computer professionals and software programmers. Author Information By Sheldon M. Ross , University of California, Berkeley, U.S.A. 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