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Nonlinear data assimilation
~
Cheng, Yuan.
Nonlinear data assimilation
Record Type:
Electronic resources : Monograph/item
Title/Author:
Nonlinear data assimilationby Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich.
Author:
Leeuwen, Peter Jan van.
other author:
Cheng, Yuan.
Published:
Cham :Springer International Publishing :2015.
Description:
xii, 116 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Simulation methods.
Online resource:
http://dx.doi.org/10.1007/978-3-319-18347-3
ISBN:
9783319183473 (electronic bk.)
Nonlinear data assimilation
Leeuwen, Peter Jan van.
Nonlinear data assimilation
[electronic resource] /by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich. - Cham :Springer International Publishing :2015. - xii, 116 p. :ill., digital ;24 cm. - Frontiers in applied dynamical systems: reviews and tutorials,v.22364-4532 ;. - Frontiers in applied dynamical systems.Reviews and tutorials ;v.2..
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
ISBN: 9783319183473 (electronic bk.)
Standard No.: 10.1007/978-3-319-18347-3doiSubjects--Topical Terms:
185954
Simulation methods.
LC Class. No.: QA401
Dewey Class. No.: 511.8
Nonlinear data assimilation
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This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
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Mathematics and Statistics (Springer-11649)
based on 0 review(s)
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EB QA401 L487 2015
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http://dx.doi.org/10.1007/978-3-319-18347-3
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