A dynamic likelihood approach to filtering-Reference-Cited by-同舟云学术

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A dynamic likelihood approach to filtering

Published:2017-10 Issue:708 Volume:143 Page:2915-2924
ISSN:0035-9009
Container-title:Quarterly Journal of the Royal Meteorological Society
language:en
Short-container-title:Q.J.R. Meteorol. Soc

Author:

Restrepo J. M.¹^ORCID

Affiliation:

1. Department of Mathematics, and College of Earth Ocean and Atmospheric Sciences; Oregon State University; Corvallis OR USA

Funder

Pacific Earthquake Engineering Research Center (PEER)

National Science Foundation NSF/OCE

Publisher

Wiley

Subject

Atmospheric Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/qj.3143/fullpdf

Reference31 articles.

1. The equivalent-weights particle filter in a high-dimensional system;Ades;Q. J. R. Meteorol. Soc.,2015

2. Accelerated Monte-Carlo for optimal estimation of time series;Alexander;J. Stat. Phys.,2005

3. Localization and the iterative ensemble Kalman smoother;Bocquet;Q. J. R. Meteorol. Soc.,2016

4. Implicit particle filters for data assimilation;Chorin;Commun. Appl. Math. Comput. Sci.,2010

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An improved framework for the dynamic likelihood filtering approach to data assimilation;Chaos: An Interdisciplinary Journal of Nonlinear Science;2022-05

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