Articoli correlati a Sequential Monte Carlo Methods in Practice
Sinossi
This volume presents results in a very active area of research of interest to statisticians, engineers, and computer scientists. The emphasis is on the applications of these important methods.
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Recensione
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 techniques discussed in this book are of great relevance to practitioners dealing with real time data." (Pradipta Sarkar, Technometrics, Vol. 45 (1), 2003)
Contenuti
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
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Sequential Monte Carlo Methods in Practice (Information Science and Statistics)
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. - Monte Carlo Methods is a very hot area of research- Book s emphasis is on applications that span many disciplines- requires only basic knowledge of probabilityMonte Carlo methods are revolutionizing the on-line analysis of data in many fileds. Codice articolo 4173359
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Sequential Monte Carlo Methods in Practice
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Springer New York, Chapman And Hall/CRC Dez 2010, 2010
ISBN 10: 1441928871
ISBN 13: 9781441928870
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 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. 616 pp. Englisch. Codice articolo 9781441928870
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Sequential Monte Carlo Methods in Practice
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Springer New York, Springer US Dez 2010, 2010
ISBN 10: 1441928871
ISBN 13: 9781441928870
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Taschenbuch. Condizione: Neu. Neuware -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 navigationrobot navigation, econometrics, financial modelling, neuralnetworks,optimal control, optimal filtering, communicationsreinforcement learning, signal enhancement, model averaging andselection, computer vision, semiconductor design, population biologydynamic 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 ParisXI 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 616 pp. Englisch. Codice articolo 9781441928870
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Sequential Monte Carlo Methods in Practice
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Springer New York, Springer US, 2010
ISBN 10: 1441928871
ISBN 13: 9781441928870
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 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. Codice articolo 9781441928870
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