Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science)

Doucet, Arnaud/ Nando DeFreitas and Neil Gordon(EDT)

Springer（2001発売）

• 製本 Hardcover:ハードカバー版／ページ数 620 p.
• 商品コード 9780387951461

Full Description

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.

Contents

1 An Introduction to Sequential Monte Carlo Methods.- 2 Particle Filters — A Theoretical Perspective.- 3 Interacting Particle Filtering With Discrete Observations.- 4 Sequential Monte Carlo Methods for Optimal Filtering.- 5 Deterministic and Stochastic Particle Filters in State-Space Models.- 6 RESAMPLE—MOVE Filtering with Cross-Model Jumps.- 7 Improvement Strategies for Monte Carlo Particle Filters.- 8 Approximating and Maximising the Likelihood for a General State-Space Model.- 9 Monte Carlo Smoothing and Self-Organising State-Space Model.- 10 Combined Parameter and State Estimation in Simulation-Based Filtering.- 11 A Theoretical Framework for Sequential Importance Sampling with Resampling.- 12 Improving Regularised Particle Filters.- 13 Auxiliary Variable Based Particle Filters.- 14 Improved Particle Filters and Smoothing.- 15 Posterior Cramér-Rao Bounds for Sequential Estimation.- 16 Statistical Models of Visual Shape and Motion.- 17 Sequential Monte Carlo Methods for Neural Networks.- 18 Sequential Estimation of Signals under Model Uncertainty.- 19 Particle Filters for Mobile Robot Localization.- 20 Self-Organizing Time Series Model.- 21 Sampling in Factored Dynamic Systems.- 22 In-Situ Ellipsometry Solutions Using Sequential Monte Carlo.- 23 Manoeuvring Target Tracking Using a Multiple-Model Bootstrap Filter.- 24 Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.- 25 Particles and Mixtures for Tracking and Guidance.- 26 Monte Carlo Techniques for Automated Target Recognition.