Probability and Statistics for Computer Scientists

Michael Baron

December 13, 2006 by Chapman and Hall/CRC
Textbook - 426 Pages - 79 B/W Illustrations
ISBN 9781584886419 - CAT# C6412

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  • Leads students from standard probability and statistics topics to stochastic processes, queuing systems, and simulations techniques
  • Describes the most commonly used types of distributions, including binomial, geometric, Poisson, uniform, exponential, gamma, and normal
  • Introduces Monte Carlo methods to estimate probabilities, expectations, and other distribution characteristics
  • Teaches how to estimate parameters of interest, test hypotheses, fit regression models, and make forecasts
  • Provides Matlab computer codes for simulation and computation
  • Contains many detailed examples and exercises that have direct applications to computer science and related fields
  • Summarizes the main concepts at the end of each chapter and reviews calculus and linear algebra in the appendix
  • Satisfies the Accreditation Board for Engineering and Technology (ABET) requirements for probability and statistics
  • Includes a solutions manual with qualifying course adoptions
  • Summary

    In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, compute probabilities and forecasts, and evaluate performance of computer systems and networks.

    After introducing probability and distributions, this easy-to-follow textbook provides two course options. The first approach is a probability-oriented course that begins with stochastic processes, Markov chains, and queuing theory, followed by computer simulations and Monte Carlo methods. The second approach is a more standard, statistics-emphasized course that focuses on statistical inference, estimation, hypothesis testing, and regression. Assuming one or two semesters of college calculus, the book is illustrated throughout with numerous examples, exercises, figures, and tables that stress direct applications in computer science and software engineering. It also provides MATLAB® codes and demonstrations written in simple commands that can be directly translated into other computer languages.

    By the end of this course, advanced undergraduate and beginning graduate students should be able to read a word problem or a corporate report, realize the uncertainty involved in the described situation, select a suitable probability model, estimate and test its parameters based on real data, compute probabilities of interesting events and other vital characteristics, and make appropriate conclusions and forecasts.


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