Overview
- Monte Carlo Methods is a very hot area of research
- Book's emphasis is on applications that span many disciplines
- requires only basic knowledge of probability
- Includes supplementary material: sn.pub/extras
Part of the book series: Information Science and Statistics (ISS)
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About this book
Monte Carlo methods are revolutionising the on-line analysis of data
in fields as diverse as financial modelling, target tracking and
computer vision. These methods, appearing under the names of bootstrap
filters, condensation, optimal Monte Carlo filters, particle filters
and survial of the fittest, have made it possible to solve numerically
many complex, non-standarard problems that were previously
intractable.
This book presents the first comprehensive treatment of these
techniques, including convergence results and applications to
tracking, guidance, automated target recognition, aircraft navigation,
robot navigation, econometrics, financial modelling, neural
networks,optimal control, optimal filtering, communications,
reinforcement learning, signal enhancement, model averaging and
selection, computer vision, semiconductor design, population biology,
dynamic Bayesian networks, and time series analysis. This will be of
great value to students, researchers and practicioners, who have some
basic knowledge of probability.
Arnaud Doucet received the Ph. D. degree from the University of Paris-
XI Orsay in 1997. From 1998 to 2000, he conducted research at the
Signal Processing Group of Cambridge University, UK. He is currently
an assistant professor at the Department of Electrical Engineering of
Melbourne University, Australia. His research interests include
Bayesian statistics, dynamic models and Monte Carlo methods.
Nando de Freitas obtained a Ph.D. degree in information engineering
from Cambridge University in 1999. He is presently a research
associate with the artificial intelligence group of the University of
California at Berkeley. His main research interests are in Bayesian
statistics and the application of on-line and batch Monte Carlo
methods to machine learning.
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Table of contents (26 chapters)
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Front Matter
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Introduction
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Front Matter
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Theoretical Issues
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Front Matter
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Strategies for Improving Sequential Monte Carlo Methods
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Front Matter
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Applications
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Front Matter
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Reviews
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"…a remarkable, successful effort at making these ideas available to statisticians. It gives an overview, presents available theory, gives a splendid development of various bells and whistles important in practical implementation, and finally gives a large number of detailed examples and case studies…The authors and editors have been careful to write in a unified, readable way…I find it remarkable that the editors and authors have combined to produce an accessible bible that will be studied and used for years to come."
"Usually, very few volumes edited from papers contributed by many different authors result in books which can serve as either good textbooks or as useful reference. However, in the case of this book, it is enough to read the foreword by Adrian Smith to realize that this particular volume is quite different. … it is a good reference book for SMC." (Mohan Delampady, Sankhya: Indian Journal of Statistics, Vol. 64 (A), 2002)
"In this book the authors present sequential Monte Carlo (SMC) methods … . Over the last few years several closely related algorithms have appeared under the names ‘boostrap filters’, ‘particle filters’, ‘Monte Carlo filters’, and ‘survival of the fittest’. The book under review brings together many of these algorithms and presents theoretical developments … . This book will be of great value to advanced students, researchers, and practitioners who want to learn about sequential Monte Carlo methods for the computational problems of Bayesian Statistics." (E. Novak, Metrika, May, 2003)
"This book provides a very good overview of the sequential Monte Carlo methods and contains many ideas on further research on methodologies and newer areas of application. … It will be certainly a valuable reference book for students and researchers working in the area of on-line data analysis. … the techniquesdiscussed in this book are of great relevance to practitioners dealing with real time data." (Pradipta Sarkar, Technometrics, Vol. 45 (1), 2003)
Editors and Affiliations
Bibliographic Information
Book Title: Sequential Monte Carlo Methods in Practice
Editors: Arnaud Doucet, Nando Freitas, Neil Gordon
Series Title: Information Science and Statistics
DOI: https://doi.org/10.1007/978-1-4757-3437-9
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2001
Hardcover ISBN: 978-0-387-95146-1Published: 21 June 2001
Softcover ISBN: 978-1-4419-2887-0Published: 01 December 2010
eBook ISBN: 978-1-4757-3437-9Published: 09 March 2013
Series ISSN: 1613-9011
Series E-ISSN: 2197-4128
Edition Number: 1
Number of Pages: XXVIII, 582
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistical Theory and Methods