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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

The weak law on large numbers


The questions from the previous section led us to limit theorems. The most important limit theorems are the (weak) law of large numbers, the theorem of (Glivenko, 1933) and (Cantelli, 1933), and the central limit theorem.

First we will have a look at the weak law of large numbers. The strong law of weak numbers is mathematically more sophisticated, but tells (almost) the same story.

The weak law of large numbers is a very intuitive concept; Jakob Bernoulli even thought this 20 years after he published it in 1713, as the golden theorem. But if we take a closer look at this law, we jump into a whole world of mathematical statistics.

The weak law of large numbers is applied in betting offices, in financial assessments and for insurance, and so on. It builds the foundation of statistics, and data scientists should be aware of it. By understanding the weak law of large numbers and the central limit theorem, one understands the basics of mathematical statistics.

Emperor...

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