The statistical observation localized equivalent-weights particle filter in a simple nonlinear model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Aquatic Science,Oceanography
Link
https://link.springer.com/content/pdf/10.1007/s13131-021-1876-1.pdf
Reference27 articles.
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3. Chen Yan, Zhang Weimin, Wang Pingqiang. 2020. An application of the localized weighted ensemble Kalman filter for ocean data assimilation. Quarterly Journal of the Royal Meteorological Society, 146(732): 3029–3047, doi: https://doi.org/10.1002/qj.3824
4. Chorin A J, Tu Xuemin. 2009. Implicit sampling for particle filters. Proceedings of the National Academy of Sciences of the United States of America, 106(41): 17249–17254, doi: https://doi.org/10.1073/pnas.0909196106
5. De Freitas N, Andrieu C, Højen-Sørensen P, et al. 2001. Sequential monte Carlo methods for neural networks. In: Doucet A, De Freitas N, Gordon N, eds. Sequential Monte Carlo Methods in Practice. New York, NY, USA: Springer, 359–379
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1. Some progress on ocean data assimilation in China: Introduction of the special section “Ocean Data Assimilation”;Acta Oceanologica Sinica;2022-02
2. The Improved Localized Equivalent-Weights Particle Filter with Statistical Observation in an Intermediate Coupled Model;Journal of Marine Science and Engineering;2021-10-20
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