Preface

"Everybody seems to think I'm lazy

I don't mind, I think they're crazy

Running everywhere at such a speed

Till they find there's no need (There's no need)"

The Beatles in their song "I'm only sleeping"

The Monte Carlo way and simulation approach are ways to stay lazy and efficient at the same time. "Lazy", since a simulation approach is generally much easier to carry out as compared to an analytical approach—there is mostly no need for analytical approaches, and one might be crazy to neglect the whole world of statistical simulation. "Efficient", since it costs minimal efforts to get reliable results, and often simulation is the only approach to get results. The simulation approach in data science and statistics is generally a more intuitive approach compared to analytical solutions. It is not hidden behind a wall of mathematics, and using a simulation approach is often the only way to solve complex problems.

Statistical simulation has thus become an essential area in data science and statistics. It can be seen as a data-driven approach to many practical problems in data science and statistics.

In this book, theory is also explained with illustrative examples using the software environment R, for which advanced data processing features are shown in the book.

This book will thus provide a computational and methodological framework for statistical simulation to users with a computational statistics and/or data science background.

More precisely, the aim of this book is to lay into the hands of the readers a book that explains methods, give advice on the usage of the methods, and provide computational tools to solve common problems in statistical simulation and computer-intense methods.

The core issues are on simulating distributions and datasets, Monte Carlo methods for inference statistics, microsimulation and dynamical systems, and presenting solutions using computer-intense approaches. You will see applications in R not only to better understand the methods but also to gain experience when working on real-world data and real-world problems.

The author of the book has tried to make humorous and amusing examples in certain chapters in order to increase interest, staying catchy and memorable. Next to serious text on methods, curious examples on individual mortality and fertility rates of the author of the book are also present as is the system dynamics from the love/hate story of Prince Henry and Chelsy Davy, the Australian guy in the Austrian mountain trying to reach the highest mountain through an optimization problem, or the weak law of winning the lottery are presented as well.