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: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Society for Industrial & Applied Mathematics (SIAM),
2018-04-09T16:05:45Z.
|
| Subjects: | |
| Online Access: | Get fulltext |