In this paper, we consider the estimation of hyperparameters for regularization terms
commonly used for obtaining structured sparse parameters in signal estimation problems …

Probabilistic graphical models encode hidden dependencies between random variables for
data modelling. Parameter estimation is a crucial part of handling such probabilistic models …

We introduce a new class of distributions named log-adjusted shrinkage priors for the
analysis of sparse signals, which extends the three parameter beta priors by multiplying an …

Structured sparsity has recently emerged in statistics, machine learning and signal
processing as a promising paradigm for learning in high-dimensional settings. All existing …

Learning redundant dictionaries for sparse representation from sets of patches has proven
its efficiency in solving inverse problems. However, the optimization process often calls for …

Sparse Bayesian learning (SBL) is a probabilistic approach to estimation problems based
on representing sparsity-promoting priors by Normals with Unknown Variances. This …

In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe distributions as
priors in the Bayesian Compressive Sensing framework is proposed. The distributions are …

Sparse Bayesian learning (SBL) models are extensively used in signal processing and
machine learning for promoting sparsity through hierarchical priors. The hyperparameters in …

Structured sparsity has recently emerged in statistics, machine learning and signal
processing as a promising paradigm for learning in high-dimensional settings. All existing …

As a well-known machine learning algorithm, sparse Bayesian learning (SBL) can find
sparse representations in linearly probabilistic models by imposing a sparsity-promoting …