Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems
From MaRDI portal
Publication:3176254
DOI10.1137/17M1121342zbMath1398.65057arXiv1703.05638MaRDI QIDQ3176254
Martin Stoll, Yue Qiu, Peter Benner
Publication date: 19 July 2018
Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1703.05638
preconditioningmatrix equationsPDE-constrained optimizationBayesian inverse problemsspace-time methodslow-rank methods
Computational methods for sparse matrices (65F50) Bayesian inference (62F15) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Control/observation systems governed by partial differential equations (93C20) Iterative numerical methods for linear systems (65F10)
Related Items
Model reduction of linear dynamical systems via balancing for Bayesian inference, Time-limited balanced truncation for data assimilation problems, Numerical linear algebra in data assimilation, Fast sampling of parameterised Gaussian random fields, Low-Rank Solution Methods for Stochastic Eigenvalue Problems, Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields
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