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...
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| Format: | Article |
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Elsevier BV
2018
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| Online Access: | http://hdl.handle.net/1721.1/116433 https://orcid.org/0000-0002-4840-8545 https://orcid.org/0000-0001-8242-3290 https://orcid.org/0000-0003-2156-9338 |