Monte Carlo strategies in scientific computing
Author(s)
Bibliographic Information
Monte Carlo strategies in scientific computing
（Springer series in statistics）
Springer, 2008, c2004
- : softcover
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"2008, First softcover printing"--T. p. verso
Includes bibliographical references and indexes
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"