Optimal Low-rank Approximations of Bayesian Linear Inverse Problems
In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computational savings can be achieved by using this subspace to characterize and approximate the posterior distribution of the param...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Society for Industrial and Applied Mathematics
2016
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| Online Access: | http://hdl.handle.net/1721.1/101896 https://orcid.org/0000-0002-5527-408X https://orcid.org/0000-0001-7359-4696 https://orcid.org/0000-0002-4840-8545 https://orcid.org/0000-0001-8242-3290 |