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Sequential Monte Carlo methods in practice

Author: Arnaud Doucet; Nando De Freitas; Neil Gordon
Publisher: New York : Springer, ©2001.
Series: Statistics for engineering and information science.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable.  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Arnaud Doucet; Nando De Freitas; Neil Gordon
ISBN: 0387951466 9780387951461 1441928871 9781441928870
OCLC Number: 45024472
Description: xxvii, 581 pages : illustrations ; 24 cm.
Contents: I: Introduction. An introduction to sequential Monte Carlo methods / Arnaud Doucet, Nando de Freitas, and Neil Gordon --
II: Theoretical issues. Particle filters --
a theoretical perspective / Dan Crisan --
Interacting particle filtering with discrete observations / Pierre Del Moral and Jean Jacod --
III: Strategies for improving sequential Monte Carlo methods. Sequential Monte Carlo methods for optimal filtering / Christophe Andrieu, Arnaud Doucet, and Elena Punskaya --
Deterministic and stochastic particle filters in state-space models / Erik Bolviken and Geir Storvik --
RESAMPLE-MOVE filtering with cross-model jumps / Carlo Berzuini and Walter Gilks --
Improvement strategies for Monte Carlo particle filters / Simon Godsill and Tim Clapp --
Approximating and maximising the likelihood for a general state-space model / Markus Hurzeler and Hans R. Kunsch --
Monte Carlo smoothing and self-organising state-space model / Genshiro Kitagawa and Seisho Sato --
Combined parameter and state estimation in simulation-based filtering / Jane Liu and Mike West --
A theoretical framework for sequential importance sampling with resampling / Jun S. Liu, Rong Chen, and Tanya Logvinenko. Improving regularised particle filters / Christian Musso, Nadia Oudjane, and Francois Le Gland --
Auxiliary variable based particle filters / Michael K. Pitt and Neil Shephard --
Improved particle filters and smoothing / Photis Stavropoulos and D.M. Titterington --
IV: Applications. Posterior Cramer-Rao bounds for sequential estimation / Niclas Bergman --
Statistical models of visual shape and motion / Andrew Blake, Michael Isard, and John MacCormick --
Sequential Monte Carlo methods for neural networks / N de Freitas [and others] --
Sequential estimation of signals under model uncertainty / Petar M. Djuric --
Particle filters for mobile robot localization / Dieter Fox [and others] --
Self-organizing time series model / Tomoyuki Higuchi --
Sampling in factored dynamic systems / Daphne Koller and Uri Lerner --
In-situ ellipsometry solutions using sequential Monte Carlo / Alan D. Marrs --
Manoeuvring target tracking using a multiple-model bootstrap filter / Shaun McGinnity and George W. Irwin --
Rao-Blackwellised particle filtering for dynamic Bayesian networks / Kevin Murphy and Stuart Russell --
Particles and mixtures for tracking and guidance / David Salmond and Neil Gordon --
Monte Carlo techniques for automated target recognition / Anuj Srivastava [and others].
Series Title: Statistics for engineering and information science.
Responsibility: Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith.
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Abstract:

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  Read more...

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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, Read more...

 
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   schema:description ""Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, 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, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, 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 practitioners, 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. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance."--"@en ;
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