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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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Toc

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

Simulating pseudo random numbers

Pseudo random numbers are the basis for almost any statistical (Monte Carlo) simulation, and they are used in a huge variety of problems in statistics. Simulating them well is crucial for valid outputs on Monte Carlo simulations, and also resampling methods rely on the quality of pseudo random numbers.

We can distinguish between (at least) two kinds of pseudo random number generators:

  • Arithmetic random number generators: They are based purely on, as the name suggests, arithmetic. Irrational numbers like Simulating pseudo random numbers and e may be used as random number generators by making use of the fractional part of any multiples used. However, it is difficult to determine whether irrational numbers, as yet, have a periodicity. In addition, irrational numbers can only be presented as (finite) machine numbers in a computer; irrational numbers are rarely used in practice to generate random numbers.
  • Recursive arithmetic random number generators: They are based on the calculation of a new...
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