Editors:
- 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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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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About this book
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
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Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia
Arnaud Doucet
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Computer Science Division, University of California, Berkeley, USA
Nando Freitas
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Pattern and Information Processing, Defence Evaluation and Research Agency, Malvern, Worcs, UK
Neil Gordon
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