Table of 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.

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