Efficient Bayesian inference for large chaotic dynamical systems

Efficient Bayesian inference for large chaotic dynamical systems

<jats:p>Abstract. Estimating parameters of chaotic geophysical models is challenging due to their inherent unpredictability. These models cannot be calibrated with standard least squares or filtering methods if observations are temporally sparse. Obvious remedies, such as averaging over tempor...

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Bibliographic Details
Main Authors: Springer, Sebastian, Haario, Heikki, Susiluoto, Jouni, Bibov, Aleksandr, Davis, Andrew, Marzouk, Youssef
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Language:English
Published: Copernicus GmbH 2022
Online Access:https://hdl.handle.net/1721.1/145429

Internet

https://hdl.handle.net/1721.1/145429

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