Sequential Monte Carlo Methods in Practice

  • Arnaud Doucet
  • Nando de Freitas
  • Neil Gordon
Part of the Statistics for Engineering and Information Science book series (ISS)

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Arnaud Doucet, Nando de Freitas, Neil Gordon
      Pages 3-14
  3. Theoretical Issues

    1. Front Matter
      Pages 15-15
    2. Pierre Del Moral, Jean Jacod
      Pages 43-75
  4. Strategies for Improving Sequential Monte Carlo Methods

    1. Front Matter
      Pages 77-77
    2. Christophe Andrieu, Arnaud Doucet, Elena Punskaya
      Pages 79-95
    3. Carlo Berzuini, Walter Gilks
      Pages 117-138
    4. Simon Godsill, Tim Clapp
      Pages 139-158
    5. Markus Hürzeler, Hans R. Künsch
      Pages 159-175
    6. Genshiro Kitagawa, Seisho Sato
      Pages 177-195
    7. Jun S. Liu, Rong Chen, Tanya Logvinenko
      Pages 225-246
    8. Christian Musso, Nadia Oudjane, Francois Le Gland
      Pages 247-271
    9. Michael K. Pitt, Neil Shephard
      Pages 273-293
    10. Photis Stavropoulos, D. M. Titterington
      Pages 295-317
  5. Applications

    1. Front Matter
      Pages 319-319

About this book

Introduction

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.

Keywords

Likelihood Monte Carlo Methods Resampling Statistical Models artificial intelligence bayesian statistics calculus data analysis dynamic models econometrics machine learning modeling neural networks statistics time series analysis

Editors and affiliations

  • Arnaud Doucet
    • 1
  • Nando de Freitas
    • 2
  • Neil Gordon
    • 3
  1. 1.Department of Electrical and Electronic EngineeringThe University of MelbourneVictoriaAustralia
  2. 2.Computer Science DivisionUniversity of CaliforniaBerkeleyUSA
  3. 3.Pattern and Information ProcessingDefence Evaluation and Research AgencyMalvern, WorcsUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3437-9
  • Copyright Information Springer-Verlag New York 2001
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-2887-0
  • Online ISBN 978-1-4757-3437-9
  • Series Print ISSN 1613-9011
  • About this book