
Probability and Computing
Randomization and Probabilistic Techniques in Algorithms and Data Analysis
- English
- PDF
- Available on iOS & Android
Probability and Computing
Randomization and Probabilistic Techniques in Algorithms and Data Analysis
About this book
Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Half title
- Title
- Copyright
- Dedication
- Contents
- Preface to the Second Edition
- Preface to the First Edition
- 1 Events and Probability
- 2 Discrete Random Variables and Expectation
- 3 Moments and Deviations
- 4 Chernoff and Hoeffding Bounds
- 5 Balls, Bins, and Random Graphs
- 6 The Probabilistic Method
- 7 Markov Chains and Random Walks
- 8 Continuous Distributions and the Poisson Process
- 9 The Normal Distribution
- 10 Entropy, Randomness, and Information
- 11 The Monte Carlo Method
- 12 Coupling of Markov Chains
- 13 Martingales
- 14 Sample Complexity, VC Dimension, and Rademacher Complexity
- 15 Pairwise Independence and Universal Hash Functions
- 16 Power Laws and Related Distributions
- 17 Balanced Allocations and Cuckoo Hashing
- Further Reading
- Index