Sequential Monte Carlo Methods in Practice

Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.

ISBN 0387951466   Volumes 1 ISBN13 9780387951461 (What's this?)   Weight (grammes) 1034 Publisher Springer-Verlag New York Inc.   Published in New York, NY Imprint Springer-Verlag New York Inc.   Series editor Green, P., Jordan, M., Jordan, M. Format Hardback   Series title Information Science and Statistics Publication date 01 Jul 2001   Height (mm) 234 Library of Congress 00047093   Width (mm) 156 DEWEY 519.282   Spine width (mm) 36 DEWEY edition DC21   Academic level Postgraduate, Professional / Scholarly Pages 595

		Foreword
		Acknowledgments
		Contributors
I		Introduction		1
1		An Introduction to Sequential Monte Carlo Methods by Arnaud Doucet and Nando de Freitas and Neil Gordon		3
II		Theoretical Issues		15
2		Particle Filters - A Theoretical Perspective by Dan Crisan		17
3		Interacting Particle Filtering With Discrete Observations by Pierre Del Moral and Jean Jacod		43
III		Strategies for Improving Sequential Monte Carlo Methods		77
4		Sequential Monte Carlo Methods for Optimal Filtering by Christophe Andrieu and Arnaud Doucet and Elena Punskaya		79
5		Deterministic and Stochastic Particle Filters in State-Space Models by Erik Bolviken and Geir Storvik		97
6		RESAMPLE-MOVE Filtering with Cross-Model Jumps by Carlo Berzuini and Walter Gilks		117
7		Improvement Strategies for Monte Carlo Particle Filters by Simon Godsill and Tim Clapp		139
8		Approximating and Maximising the Likelihood for a General State-Space Model by Markus Hurzeler and Hans R. Kunsch		159
9		Monte Carlo Smoothing and Self-Organising State-Space Model by Genshiro Kitagawa and Seisho Sato		177
10		Combined Parameter and State Estimation in Simulation-Based Filtering by Jane Liu and Mike West		197
11		A Theoretical Framework for Sequential Importance Sampling with Resampling by Jun S. Liu and Rong Chen and Tanya Logvinenko		225
12		Improving Regularised Particle Filters by Christian Musso and Nadia Oudjane and Francois Le Gland		247
13		Auxiliary Variable Based Particle Filters by Michael K. Pitt and Neil Shephard		273
14		Improved Particle Filters and Smoothing by Photis Stavropoulos and D. M. Titterington		295
		More...

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 techniques discussed in this book are of great relevance to practitioners dealing with real time data." (Pradipta Sarkar, Technometrics, Vol. 45 (1), 2003)

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