Book contents
- Frontmatter
- Contents
- Chapter dependencies
- 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
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Chapter dependencies
- 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
Summary
Intended audience
This book is a primary text for graduate-level courses in probability and random processes that are typically offered in electrical and computer engineering departments. The text starts from first principles and contains more than enough material for a two-semester sequence. The level of the text varies from advanced undergraduate to graduate as the material progresses. The principal prerequisite is the usual undergraduate electrical and computer engineering course on signals and systems, e.g., Haykin and Van Veen or Oppenheim and Willsky (see the Bibliography at the end of the book). However, later chapters that deal with random vectors assume some familiarity with linear algebra; e.g., determinants and matrix inverses.
How to use the book
A first course. In a course that assumes at most a modest background in probability, the core of the offering would include Chapters 1–5 and 7. These cover the basics of probability and discrete and continuous random variables. As the chapter dependencies graph on the preceding page indicates, there is considerable flexibility in the selection and ordering of additional material as the instructor sees fit.
A second course. In a course that assumes a solid background in the basics of probability and discrete and continuous random variables, the material in Chapters 1–5 and 7 can be reviewed quickly.
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2006