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

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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Bibliographic Details
Main Authors: Nan Chen, Andrew J. Majda
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Entropy
Subjects:
conditional Gaussian systems
multiscale nonlinear stochastic systems
physics-constrained nonlinear stochastic models
stochastically coupled reaction–diffusion models
conditional Gaussian mixture
superparameterization
conformation theory
model error
hybrid strategy
parameter estimation
Online Access:http://www.mdpi.com/1099-4300/20/7/509

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