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Deterministic and Stochastic Particle Filters in State-Space Models by Doucet, Arnaud; Freitas, Nando; Gordon, Neil is a scholarly article available to read on EtoBox.

What is Deterministic and Stochastic Particle Filters in State-Space Models about?

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

Author

Doucet, Arnaud; Freitas, Nando; Gordon, Neil

Publisher

Springer New York

Published

2001

Language

EN

ISBN

9781475734379