Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models
Author:
Affiliation:
1. Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106 USA.
Funder
Division of Mathematical Sciences
John Simon Guggenheim Memorial Foundation
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Reference32 articles.
1. A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
2. Analysis of the Gibbs Sampler for Hierarchical Inverse Problems
3. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
4. Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis
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