This paperback edition is a reprint of the 2001 Springer edition. 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 the textbook for a graduate-level course on Monte Carlo methods. Many problems discussed in the alter chapters can be potential thesis topics for masters’ or Ph.D. students in statistics or computer science departments. The author is a leading researcher in a very active area of research. Emphasis is on making these methods accessible to scientists who wantto apply them. Includes examples from artificial intelligence, computational biology, computer vision and chemistry INDICE: 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. Basics in Probability and Statistics. References. Author Index. Subject Index.
- ISBN: 978-0-387-76369-9
- Editorial: Springer
- Encuadernacion: Rústica
- Páginas: 360
- Fecha Publicación: 01/02/2008
- Nº Volúmenes: 1
- Idioma: Inglés