Monte Carlo methods are revolutionising the on-line analysis of datain fields as diverse as financial modelling, target tracking andcomputer vision. These methods, appearing under the names of bootstrapfilters, condensation, optimal Monte Carlo filters, particle filtersand survial of the fittest, have made it possible to solve numericallymany complex, non-standarard problems that were previouslyintractable.This book presents the first comprehensive treatment of thesetechniques, including convergence results and applications totracking, guidance, automated target recognition, aircraft navigationrobot navigation, econometrics, financial modelling, neuralnetworks,optimal control, optimal filtering, communicationsreinforcement learning, signal enhancement, model averaging andselection, computer vision, semiconductor design, population biologydynamic Bayesian networks, and time series analysis. This will be ofgreat value to students, researchers and practicioners, who have somebasic knowledge of probability.Arnaud Doucet received the Ph. D. degree from the University of ParisXI Orsay in 1997. From 1998 to 2000, he conducted research at theSignal Processing Group of Cambridge University, UK. He is currentlyan assistant professor at the Department of Electrical Engineering ofMelbourne University, Australia. His research interests includeBayesian statistics, dynamic models and Monte Carlo methods.Nando de Freitas obtained a Ph.D. degree in information engineeringfrom Cambridge University in 1999. He is presently a researchassociate with the artificial intelligence group of the University ofCalifornia at Berkeley. His main research interests are in Bayesianstatistics and the application of on-line and batch Monte Carlomethods to machine learning.

This volume presents results in a very active area of research ofinterest to statisticians, engineers, and computer scientists. Theemphasis is on the applications of these important methods.

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.