The theory of probability has important applications for computer and electrical engineers such that a course in probability and random processes is a prerequisite for further study in communications or signal processing. Gubner presents a primary text that progresses from advanced undergraduate level, through to more complex topics suitable for graduates and as reference material for researchers. With chapter outlines, over 300 worked examples, some 800 problems and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students.
The theory of probability has important applications for computer and electrical engineers such that a course in probability and random processes is a prerequisite for further study in communications or signal processing. Gubner presents a primary text that progresses from advanced undergraduate level, through to more complex topics suitable for graduates and as reference material for researchers. With chapter outlines, over 300 worked examples, some 800 problems and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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