Probability and Random Processes for Electrical and Computer Engineers
eBook - PDF

Probability and Random Processes for Electrical and Computer Engineers

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Probability and Random Processes for Electrical and Computer Engineers

About this book

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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Yes, you can access Probability and Random Processes for Electrical and Computer Engineers by John A. Gubner in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Signals & Signal Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. Chapter dependencies
  8. Preface
  9. 1 Introduction to probability
  10. 2 Introduction to discrete random variables
  11. 3 More about discrete random variables
  12. 4 Continuous random variables
  13. 5 Cumulative distribution functions and their applications
  14. 6 Statistics
  15. 7 Bivariate random variables
  16. 8 Introduction to random vectors
  17. 9 Gaussian random vectors
  18. 10 Introduction to random processes
  19. 11 Advanced concepts in random processes
  20. 12 Introduction to Markov chains
  21. 13 Mean convergence and applications
  22. 14 Other modes of convergence
  23. 15 Self similarity and long-range dependence
  24. Bibliography
  25. Index