Information Science and Statistics

Sequential Monte Carlo Methods in Practice

Editors: Doucet, Arnaud, Freitas, Nando de, Gordon, Neil (Eds.)

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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.

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


Table of contents (26 chapters)

  • An Introduction to Sequential Monte Carlo Methods

    Doucet, Arnaud (et al.)

    Pages 3-14

  • Particle Filters — A Theoretical Perspective

    Crisan, Dan

    Pages 17-41

  • Interacting Particle Filtering With Discrete Observations

    Moral, Pierre (et al.)

    Pages 43-75

  • Sequential Monte Carlo Methods for Optimal Filtering

    Andrieu, Christophe (et al.)

    Pages 79-95

  • Deterministic and Stochastic Particle Filters in State-Space Models

    Bølviken, Erik (et al.)

    Pages 97-116

Buy this book

eBook $179.00
price for USA (gross)
  • ISBN 978-1-4757-3437-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $229.00
price for USA
  • ISBN 978-0-387-95146-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $229.00
price for USA
  • ISBN 978-1-4419-2887-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Sequential Monte Carlo Methods in Practice
Editors
  • Arnaud Doucet
  • Nando de Freitas
  • Neil Gordon
Series Title
Information Science and Statistics
Copyright
2001
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4757-3437-9
DOI
10.1007/978-1-4757-3437-9
Hardcover ISBN
978-0-387-95146-1
Softcover ISBN
978-1-4419-2887-0
Series ISSN
1613-9011
Edition Number
1
Number of Pages
XXVIII, 582
Topics