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