Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Agent-based models


 

"Prediction is very difficult, especially if it's about the future."

 
 --– Niels Bohr

This quotation gives a warning about forecasting in the future. This quotation states problems about the traditional approach to forecasting - the prediction of estimators/summary statistics. However, agent-based models (microsimulation) provide prediction for each single individual in the future. In following sections, we will show some background into how individual predictions used to be done.

Microsimulation models are favored in the area of demographics for population forecasts, simulating the spread of diseases, and to forecast social or economic changes. In population statistics three continuous time scales are important, the individual's age, time, and the time that an individual has already spent in a specific state.

The input of such a stochastic model is a population of time transition rates, and possibly a population of migrants. We are interested in the state of times .

Optimally...