John A. Gubner

Probability and Random Processes for Electrical and Computer Engineers (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The theory of probability is a powerful tool that helps electrical and computer engineers to explain, model, analyze, and design the technology they develop. The text begins at the advanced undergraduate level, assuming only a modest knowledge of probability, and progresses through more complex topics mastered at graduate level. The first five chapters cover the basics of probability and both discrete and continuous random variables. The later chapters have a more specialized coverage, including random vectors, Gaussian random vectors, random processes, Markov Chains, and convergence. Describing tools and results that are used extensively in the field, this is more than a textbook; it is also a reference for researchers working in communications, signal processing, and computer network traffic analysis. With over 300 worked examples, some 800 homework problems, and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students. Further resources for this title, including solutions (for Instructors only), are available online at www.cambridge.org/9780521864701.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 1. Juni 2006
• Englisch
• ISBN-13: 9781107486522
• Artikelnr.: 52972039

Autorenporträt

John A. Gubner received his PhD in 1988 from the University of Maryland, College Park, after which he joined the University of Wisconsin, Madison, where he is currently a faculty member in the Department of Electrical and Computer Engineering. His research interests include ultra-wideband communications, point processes and shot noise, subspace methods in statistical processing, and information theory.

Inhaltsangabe

Preface
1. Introduction to probability
2. Introduction to discrete random variables
3. More about discrete random variables
4. Continuous random variables
5. Cumulative distribution functions and their applications
6. Statistics
7. Bivariate random variables
8. Introduction to random vectors
9. Gaussian random vectors
10. Introduction to random processes
11. Advanced concepts in random processes
12. Introduction to Markov chains
13. Mean convergence and applications
14. Other modes of convergence
15. Self similarity and long-range dependence
Bibliography
Index.