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.

The advent of cheap and massive computational power in conjunction with developments in applied statistics have stimulated many advancements in the field of sequential Monte Carlo simulation. This text examines some of the applications.

Includes bibliographical references (p. [553]-576) and index.

Tutorial Chapter * Particle Filters - A Theoretical Perspective *

Interacting Particle System Approximation Methods for Feynman-Kac

Formulae and Nonlinear Filtering * Interacting Parallel Chains for

Sequential Bayesian Estimation * Stochastic and Deterministic Particle

Filters * Super-Efficient Particle Filters for Tracking Problems *

Following a Moving Target - Monte Carlo Inference for Dynamic Bayesian

Models * Improvement Strategies for Particle Filters with Examples

from Communications and Audio Signal Processing * Approximating and

Maximizing the Likelihood for a General State Space Model * Analysis

and Implementation Issues of Regularized Particle Filters * Combined

Parameter and State Estimation in Simulation-based Filtering *

Sequential Importance Sampling * Auxiliary Variable Based Particle

Filters * Improved Particle Filters and Smoothing * Terrain Navigation

Using Sequential Monte Carlo Methods * Statistical Models of Visual

Shape and Motion * Sequential Monte Carlo Methods for Neural Networks

* Short Term Forecasting of Electricity Load * Particles and Mixtures

for Tracking and Guidance * Monte Carlo Filter Approach to an Analysis

of Small Count Time Series * Monte Carlo Smoothing and Self-Organizing