Monte Carlo Strategies in Scientific Computing [Paperback]

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be standardized and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

This book provides an up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be standardized and compared. It can be used as a textbook for a graduate-level course on Monte Carlo methods.

Introduction and examples.- Basic principles: rejection, weighting, and others.- Theory of sequential Monte Carlo.- Sequential Monte Carlo in action.- Metropolis algorithm and beyond.- The Gibbs sampler.- Cluster algorithms for the Ising model.- General conditional sampling.- Molecular dynamics and hybrid Monte Carlo.- Multilevel sampling and optimization methods.- Population-based Monte Carlo methods.- Markov chains and their convergence.- Selected theoretical topics.

From the reviews:

MATHEMATICAL REVIEWS

This book is an excellent survey of current Monte Carlo methods. A strength of the book is the inclusion of a number of applications to current scientific problems. The applications amply demonstrate the relevance of this approach to modern computing. There is a fairly thorough coverage of wide variety of Monte Carlo algorithms that have arisen in diverse fields such as physics, chemistry, biology, etc., and the relationship among them. The book is highly recommended.

SHORT BOOK REVIEWS

This is a worthwhile reference to recent l3/