Sequential Monte Carlo Methods in Pratice

Arnaud Doucet, Nando De Freitas, Neil Gordon - Collection Statistics for Engineering and Information Science

582 pages, parution le 01/03/2001

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Résumé

Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard 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 modeling, 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 practitioners, who have some basic knowledge of probability.

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Sommaire

Introduction
Theoretical Issues
Strategies for Improving Sequential Monte Carlo Methods
Applications

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Arnaud Doucet, Nando De Freitas, Neil Gordon
Collection	Statistics for Engineering and Information Science
Parution	01/03/2001
Nb. de pages	582
Format	16 x 24
Couverture	Relié
Poids	995g
Intérieur	Noir et Blanc
EAN13	9780387951461
ISBN13	978-0-387-95146-1