9783030478445 - an introduction to sequential monte carlo (springer series in statistics) von chopin, nicolas; papaspiliopoulos, omiros (4 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2020
Serie: Springer Series in Statistics, Buch 155 von 160. Buch 155 von 160 - Springer Series in Statistics
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2020
Serie: Springer Series in Statistics, Buch 155 von 160. Buch 155 von 160 - Springer Series in Statistics
- Hardcover
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Sprache: Englisch
Verlag: Springer Nature, 2020
Serie: Springer Series in Statistics, Buch 155 von 160. Buch 155 von 160 - Springer Series in Statistics
- Hardcover
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Hardcover. Zustand: Brand New. 402 pages. 9.25x6.10x0.98 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland, 2020
Serie: Springer Series in Statistics, Buch 155 von 160. Buch 155 von 160 - Springer Series in Statistics
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine lear…ning, population ecology, quantitative finance, and robotics.The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book.Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed.The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a 'Python corner,' which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.
