Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV,
2018-06-19T19:16:49Z.
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| Subjects: | |
| Online Access: | Get fulltext |