Book contents
- Frontmatter
- Frontmatter
- Contents
- Foreword
- Preface
- Appetizer Using Probability to Cover a Geometric Set
- 1 Preliminaries on Random Variables
- 2 Concentration of Sums of Independent Random Variables
- 3 Random Vectors in High Dimensions
- 4 Random Matrices
- 5 Concentration Without Independence
- 6 Quadratic Forms, Symmetrization, and Contraction
- 7 Random Processes
- 8 Chaining
- 9 Deviations of Random Matrices and Geometric Consequences
- 10 Sparse Recovery
- 11 Dvoretzky–Milman Theorem
- Hints for Exercises
- References
- Index
7 - Random Processes
Published online by Cambridge University Press: 29 September 2018
- Frontmatter
- Frontmatter
- Contents
- Foreword
- Preface
- Appetizer Using Probability to Cover a Geometric Set
- 1 Preliminaries on Random Variables
- 2 Concentration of Sums of Independent Random Variables
- 3 Random Vectors in High Dimensions
- 4 Random Matrices
- 5 Concentration Without Independence
- 6 Quadratic Forms, Symmetrization, and Contraction
- 7 Random Processes
- 8 Chaining
- 9 Deviations of Random Matrices and Geometric Consequences
- 10 Sparse Recovery
- 11 Dvoretzky–Milman Theorem
- Hints for Exercises
- References
- Index
- Type
- Chapter
- Information
- High-Dimensional ProbabilityAn Introduction with Applications in Data Science, pp. 147 - 175Publisher: Cambridge University PressPrint publication year: 2018