Monte Carlo Strategies In Scientific Computing

This paperback edition is a reprint of the 2001 Springer edition. This bookprovides a selfcontained and uptodate treatment of the Monte Carlo methodand develops... » 

This paperback edition is a reprint of the 2001 Springer edition. This bookprovides a selfcontained and uptodate treatment of the Monte Carlo methodand develops a common framework under which various Monte Carlo techniques canbe standardized and compared. Given the interdisciplinary nature of thetopics and a moderate prerequisite for the reader this book should be ofinterest to a broad audience of quantitative researchers such as computationalbiologists computer scientists econometricians engineers probabilists andstatisticians. It can also be used as the textbook for a graduatelevel courseon Monte Carlo methods. Many problems discussed in the alter chapters can bepotential thesis topics for masters or Ph.D. students in statistics orcomputer science departments. TOCIntroduction and Examples. Basic PrinciplesRejection Weighting and Others. Theory of Sequential Monte Carlo.Sequential Monte Carlo in Action. Metropolis Algorithm and Beyond. The GibbsSampler. Cluster Algorithms for the Ising Model. General ConditionalSampling. Molecular Dynamics and Hybrid Monte Carlo. Multilevel Sampling andOptimization Methods. PopulationBased Monte Carlo Methods. Markov Chains andTheir Convergence. Selected Theoretical Topics. Basics in Probability andStatistics. References. Author Index. Subject Index. «