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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

Summary


The basic concept of system dynamics is to predict time based on given scenarios/assumptions.

We looked at simple applications for population demographics using microsimulation modeling. Microsimulation modeling became popular since managers and politicians wanted to have predictions of the future. While this was done in the past with aggregated information, with agent-based microsimulation models we do it on an individual level. A criticism of this is that statistical uncertainty is not taken into account and that selected scenarios are unlikely to be true in the future since political changes or unobservable events may happen that cannot be considered beforehand.

Dynamic systems are popular in business and finance but they also play a central role in ecological research. We looked at the Lotka-Volterra model in one example that the author of this book regularly observes in a place in the Upper Austrian mountains. But most importantly, and ironically speaking, we have shown the relationship...

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