Doucet, Arnaud, Freitas, Nando de, Gordon, Neil (Eds.)
2001, XXVIII, 582 p.
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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
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.
Content Level »Research
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
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 State Space Model * Sequential Monte Carlo Methods Applied to Graphical Models * In-situ Ellipsometry * Maneuvering Target Tracking Using a Multiple Model Bootstrap Filter * Particle Filters and Diagnostic Checking in Time Series * MCMC Estimation on Transformation Groups for Object Recognition