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Nonlinear Data Assimilation : Mathematics and Statistics (R0) - Peter Jan Van Leeuwen

Nonlinear Data Assimilation

By: Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich

eText | 22 July 2015

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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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Non-FictionMathematicsApplied MathematicsNonlinear ScienceCalculus & Mathematical AnalysisNumerical AnalysisMathematical ModellingOptimisationLinear ProgrammingReference, Information & Interdisciplinary SubjectsResearch & InformationInformation theory
Cybernetics & Systems TheorySciencePhysicsMathematical PhysicsLibrary & Info SciencesLibrary & Information Services

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