Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussian features of nature. The special structure of th...

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Detalles Bibliográficos
Autores principales: Chen, Nan, Majda, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513031/
https://www.ncbi.nlm.nih.gov/pubmed/33265599
http://dx.doi.org/10.3390/e20070509

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513031/
https://www.ncbi.nlm.nih.gov/pubmed/33265599
http://dx.doi.org/10.3390/e20070509

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