A comprehensive introduction to sampling-based methods in statistical
computing
The use of computers in mathematics and statistics has opened up a wide
range of techniques for studying otherwise intractable problems.
Sampling-based simulation techniques are now an invaluable tool for
exploring statistical models. This book gives a comprehensive
introduction to the exciting area of sampling-based methods.
An Introduction to Statistical Computing introduces the classical
topics of random number generation and Monte Carlo methods. It also
includes some advanced methods such as the reversible jump Markov chain
Monte Carlo algorithm and modern methods such as approximate Bayesian
computation and multilevel Monte Carlo techniques
An Introduction to Statistical Computing
- Fully covers the traditional topics of statistical computing.
- Discusses both practical aspects and the theoretical background.
- Includes a chapter about continuous-time models.
- Illustrates all methods using examples and exercises.
- Provides answers to the exercises (using the statistical computing
environment R); the corresponding source code is available online.
- Includes an introduction to programming in R.
This book is mostly self-contained; the only prerequisites are basic
knowledge of probability up to the law of large numbers. Careful
presentation and examples make this book accessible to a wide range of
students and suitable for self-study or as the basis of a taught course.
