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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Chapter 6. Probability Theory Shown by Simulation

 

"I cannot believe that God plays dice with the cosmos."

 
 --– Albert Einstein

This section includes simulation experiments to show some aspects of probability theory and mathematical statistics. It will turn out that simulations make it easy to understand basic theorems. The weak law of large numbers, but especially the central limit theorem, are discussed in detail. The latter is probably the most important theorem in statistics and worth discussing in detail. First, some very well-known basics are introduced.

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