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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

Probability distributions


We take a small excursus to probability distributions, since they are used frequently in the book. The theoretical distributions are, both in descriptive and in mathematical statistics, of central importance:

  • To approximate functions, such as a description of empirically observed frequency distributions in descriptive statistics

  • The determination of probabilities for results of certain random experiments in mathematical statistics

Some important theoretical distributions are, for example, the Binomial distribution, the Poisson distribution, the hyper-geometrical distribution, the uniform distribution, the exponential distribution, the normal distribution, the distribution, and the t distribution.

Discrete probability distributions

Married or not married, in the Austrian population, defines already a discrete probability distribution. Discrete distributions are in general very important and we should take a closer look at them, since they are needed in the following...

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