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  Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems

Benner, P., Qiu, Y., & Stoll, M. (2018). Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification, 6(2), 965-989. doi:10.1137/17M1121342.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-2E2A-F Version Permalink: http://hdl.handle.net/21.11116/0000-0002-F4FC-F
Genre: Journal Article

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© 2018, Society for Industrial and Applied Mathematics. This publication is with permission of the rights owner freely accessible on MPG.PuRe.
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 Creators:
Benner, Peter1, Author              
Qiu, Yue1, Author              
Stoll, Martin2, Author              
Affiliations:
1Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141              
2Numerical Linear Algebra for Dynamical Systems, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1832293              

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Free keywords: Mathematics, Numerical Analysis, math.NA
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 Dates: 2018
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1137/17M1121342
arXiv: 1703.05638
URI: http://arxiv.org/abs/1703.05638
Other: pubdata_escidoc:2474802
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Title: SIAM/ASA Journal on Uncertainty Quantification
Source Genre: Journal
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Pages: - Volume / Issue: 6 (2) Sequence Number: - Start / End Page: 965 - 989 Identifier: -