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

Some basics on probability theory


Probability theory is a branch of mathematics, and it forms the basics to infer from a sample to a population. Together with the field of analytical statistics, probability theory is used in the field of stochastic to describe random events. Stochastic modeling in turn uses probabilistic concepts—randomness and laws regarding randomness—for the modeling and analysis of real random processes (for example, in economic forecasting). Let's introduce some notation and basic concepts.

A random process or random experiment is any procedure that can be infinitely repeated and has a well-defined set of possible outcomes. For example, rolling a die is a random experiment.

The set of outcomes is denoted by . These are all possible outcomes of the random experiment. Example: for rolling a die, .

A random variable, , can take on a set of possible different values (by chance), each with an associated probability.

The output of the random experiment is a random variable. Example...