Table of Contents (128k)
Preface (162k)
Chapter 1: Sampling, Statistics and Computer Code (1,516k)

This book teaches modern Markov chain Monte Carlo (MC) simulation techniques step by step. The material should be accessible to advanced undergraduate students and is suitable for a course. It ranges from elementary statistics concepts (the theory behind MC simulations), through conventional Metropolis and heat bath algorithms, autocorrelations and the analysis of the performance of MC algorithms, to advanced topics including the multicanonical approach, cluster algorithms and parallel computing. Therefore, it is also of interest to researchers in the field. The book relates the theory directly to Web-based computer code. This allows readers to get quickly started with their own simulations and to verify many numerical examples easily. The present code is in Fortran 77, for which compilers are freely available. The principles taught are important for users of other programming languages, like C or C++.

Contents:

• Sampling, Statistics and Computer Code
• Error Analysis for Independent Random Variables
• Markov Chain Monte Carlo
• Error Analysis for Markov Chain Data
• Advanced Monte Carlo
• Parallel Computing
• Conclusions, History and Outlook

“... a special feature of this book is inclusion of computer code directly related to the theory of the techniques ... it allows readers to get quickly started with their own simulations and to verify numerical examples easily ... a number of assignments in almost every section and exercises in the Appendix also help in readers' understanding.”