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dc:title: Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems | SIAM/ASA Journal on Uncertainty Quantification | Vol. 6, No. 2 | Society for Industrial and Applied Mathematics
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dc:description: SIAM/ASA J. Uncertainty Quantification 2018.6:965-989
Keywords: Bayesian inverse problems,PDE-constrained optimization,low-rank methods,space-time methods,preconditioning,matrix equations
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title: Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems | SIAM/ASA Journal on Uncertainty Quantification | Vol. 6, No. 2 | Society for Industrial and Applied Mathematics
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