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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

Index

A

  • aes(). assignment / The ggplot2 package
  • aesthetic mapping / The ggplot2 package
  • agent-based modeling / What is simulation and where is it applied?
  • agent-based modeling (ABM) / Choosing the right simulation technique
  • agent-based models
    • about / Agent-based models
  • alias method / The alias method
  • arithmetic random number generators / Simulating pseudo random numbers

B

  • Beta distribution / Simulating random numbers from a Beta distribution
  • BFGS method / Further general-purpose optimization methods
  • bias
    • estimating, bootstrap used / Estimating bias with bootstrap
  • Bias Corrected alpha (BCa) confidence interval method / Confidence intervals by bootstrap
  • Big Boss 2 approach / Why the bootstrap works
  • Big Boss approach / Why the bootstrap works
  • bootstrap / Why use simulation?
    • about / The bootstrap, A closer look at the bootstrap
    • motivating example...
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