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 navigation robot navigation econometrics financial
modelling neuralnetworks optimal control optimal filtering communications reinforcement
learning signal enhancement model averaging andselection computer vision semiconductor
design population biology dynamic 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 Paris-XI 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.
