Leveraging Joint Sparsity in Hierarchical Bayesian Learning-Reference-Cited by-同舟云学术

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Leveraging Joint Sparsity in Hierarchical Bayesian Learning

Published:2024-05-24 Issue:2 Volume:12 Page:442-472
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Glaubitz Jan¹^ORCID,Gelb Anne²^ORCID

Affiliation:

1. Corresponding author. Department of Aeronautics and Astronautics & Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.

2. Department of Mathematics, Dartmouth College, Hanover, NH 03755 USA.

Funder

Air Force Office of Scientific Research

Multidisciplinary University Research Initiative

U.S. Department of Energy

Division of Mathematical Sciences

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference47 articles.

1. Joint Sparse Recovery Based on Variances

2. First-Order Methods in Optimization

3. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

4. A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning

5. Bayes Meets Krylov: Statistically Inspired Preconditioners for CGLS

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