An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems
We study Bayesian inference methods for solving linear inverse problems, focusing on hierarchical formulations where the prior or the likelihood function depend on unspecified hyperparameters. In practice, these hyperparameters are often determined via an empirical Bayesian method that maximizes the...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2019
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| Online Access: | https://hdl.handle.net/1721.1/122927 |