Probability Models For Computer 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 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

Audience: Computer professionals and software programmers.

The role of probability in computer science is growing and, in lieu of a tailored book, many professionals employ a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer scientists, probability and statistics sage Sheldon Ross has developed the premier probability title for computer scientists involved in computer simulation and modeling.

A clear understanding of the nature of probability modeling is an essential task in developing computer systems and software.

The math is precise and easily understood. As with his other best-selling titles, 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) topics.

A key feature of this book is its many interesting examples and exercises that have been chosen to illuminate the techniques presented. For instance, there are examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organizing list problems, antichains, minimal and maximal cuts in graphs, random permutations, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queing networks, distributed workload models, and more.

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.