Probability Models for Computer Science
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Probability Models for Computer Science

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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.
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Product details

  • Mixed media product | 304 pages
  • 152.4 x 228.6 x 22.86mm | 521.63g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0125980515
  • 9780125980517

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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About 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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