Bayesian Inverse Problems with L[subscript 1] Priors: A Randomize-Then-Optimize Approach
Prior distributions for Bayesian inference that rely on the L[subscript 1]-norm of the parameters are of considerable interest, in part because they promote parameter fields with less regularity than Gaussian priors (e.g., discontinuities and blockiness). These L[subscript 1]-type priors include the...
| Main Authors: | , , , , |
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| Format: | Article |
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Society for Industrial & Applied Mathematics (SIAM)
2018
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| Online Access: | http://hdl.handle.net/1721.1/114625 https://orcid.org/0000-0002-4478-2468 https://orcid.org/0000-0001-8242-3290 |