Sparse reconstructions from few noisy data: analysis of hierarchical Bayesian models with generalized gamma hyperpriors
type: Item
instance of: scholarly article
title: Sparse reconstructions from few noisy data: analysis of hierarchical Bayesian models with generalized gamma hyperpriors
zbMATH Open document ID: 1464.62365
author: Monica Pragliola
author: Erkki Somersalo
author: Alexander Strang
author: Daniela Calvetti
published in: Inverse Problems
publication date: 2020-02-03T00:00:00Z
Mathematics Subject Classification ID: 62L20
Mathematics Subject Classification ID: 65F10
zbMATH DE Number: 7161826
zbMATH Keywords: convexity
zbMATH Keywords: optimization
zbMATH Keywords: Krylov subspace
zbMATH Keywords: bound constraints
MaRDI profile type: Publication
full work available at URL: ab4d92
OpenAlex ID: W2980939245
cites work: Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems
cites work: Sparsity regularization in inverse problems
cites work: Stable signal recovery from incomplete and inaccurate measurements
cites work: Preconditioned iterative methods for linear discrete ill-posed problems from a Bayesian inversion perspective
cites work: Priorconditioners for linear systems
cites work: Conditionally Gaussian Hypermodels for Cerebral Source Localization
cites work: Hypermodels in the Bayesian imaging framework
cites work: Hierachical Bayesian models and sparsity: ℓ 2 -magic
cites work: A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning
cites work: Bayes Meets Krylov: Statistically Inspired Preconditioners for CGLS
cites work: Priorconditioned CGLS-Based Quasi-MAP Estimate, Statistical Stopping Rule, and Ranking of Priors
cites work: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
cites work: Functional error estimators for the adaptive discretization of inverse problems
cites work: Iteratively reweighted least squares minimization for sparse recovery
cites work: Convergence of an Iterative Method for Total Variation Denoising
cites work: For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
cites work: Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
cites work: Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
cites work: Methods of conjugate gradients for solving linear systems
cites work: Q4284850
cites work: Sparsity-promoting Bayesian inversion
cites work: Q5728711
cites work: Sparse Approximate Solutions to Linear Systems
cites work: Proximal Markov chain Monte Carlo algorithms
cites work: Q4864293
cites work: Lower bounds on the maximum cross correlation of signals (Corresp.)
DOI: 10.1088/1361-6420/AB4D92
description: scientific article; zbMATH DE number 7161826