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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
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Archana Tikayat Ray
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Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

Random variables, means, and variance

Informally, we can say that random variables are functions that map outcomes to numerical values. Since the probability measure assigns probabilities to outcomes and events, we may define the probability that a random variable equals certain values. The technical definition is as follows.

Definition – random variable

A function X: S → R, where R is a discrete set, is a discrete random variable (RV).

Important Note

The other main class of RVs is continuous RVs, which take values in R or some other uncountable set instead of just a discrete set, but they are outside the scope of this book.

Example – data transfer errors

Data transferred over digital communication channels are, at the lowest level, a stream of binary digits. Sometimes there can be noise or other distortions that cause errors in their transmission. It is important to quantify the errors, but it is random, so the best we can do is estimate the...

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