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Simulation for Data Science with R

Simulation for Data Science with R

By : Matthias Templ
4.2 (5)
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Simulation for Data Science with R

Simulation for Data Science with R

4.2 (5)
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 (13 chapters)
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12
Index

What this book covers

Chapter 1, Introduction, discusses the general aim of simulation experiments in data science and statistics, why and where simulation is used, and the special case of dealing with big data.

Chapter 2, R and High-Performance Computing, consists of comprehensive text on advanced computing, data manipulation, and visualization with R.

Chapter 3, The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions, reports problems on numerical precision, rounding, and convergence in a deterministic setting.

Chapter 4, Simulation of Random Numbers, starts with the simulation of uniform random numbers and transformation methods to obtain other kinds of distributions. It includes a discussion of various types of Markov chain Monte Carlo (MCMC) methods.

Chapter 5, Monte Carlo Methods for Optimization Problems, introduces deterministic and stochastic optimization methods.

Chapter 6, Probability Theory Shown by Simulation, has a strong focus on basic theorems in statistics; for example, the concept of the weak law of large numbers and the central limit theorem are shown by simulation.

Chapter 7, Resampling Methods, is a comprehensive view on the bootstrap, the jackknife and cross-validation.

Chapter 8, Applications of Resampling Methods and Monte Carlo Tests, shows applications in various fields such as regression, imputation, and time series analysis. In addition, Monte Carlo tests and their variants such as permutation tests and bootstrap tests are presented.

Chapter 9, The EM Algorithm, introduces the expectation maximum method to iteratively obtain an optima. Applications in clustering and imputation of missing values are given.

Chapter 10, Simulation with Complex Data, shows how to simulate synthetic data as well as population data that can be used for the comparison of methods in general or also serve as input for agent-based microsimulation models.

Chapter 11, System Dynamics and Agent-Based Models, discusses agent-based microsimulation models and shows basic models in system dynamics to study complex dynamical systems.

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