Probability and statistics for computer scientists

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Title:

Probability and statistics for computer scientists

Author:

Baron, Michael, 1968-

ISBN:

9781584886419

Personal Author:

Baron, Michael, 1968-

Publication Information:

Boca Raton, Fla. ; London : Chapman & Hall/CRC, 2007.

Physical Description:

xi, 413 p. ; 23 cm.

General Note:

Formerly CIP.

Contents:

1. Introduction and overview -- 2. Probability -- 3. Discrete random variables and their distributions -- 4. Continuous distributions -- 5. Computer simulations and Monte Carlo methods -- 6. Stochastic processes -- 7. Queuing systems -- 8. Introduction to statistics -- 9. Statistical inference -- 10. Regression -- 11. Appendix.

Subject Term:

Probabilities -- Computer simulation.

Mathematical statistics -- Computer simulation.

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025509163	519.20113 B265P 2007	1	Searching... Unknown

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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.