An Introduction to Statistical Computing

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.

Table of Contents

Chapter 1 Random number generation
Chapter 2 Simulating statistical models
Chapter 3 Monte Carlo methods
Chapter 4 Markov Chain Monte Carlo methods
Chapter 5 Beyond Monte Carlo
Chapter 6 Continuous-time models
Appendix A Probability reminders
Appendix B Programming in R
Appendix C Answers to the exercises