Giant hailstones measuring six inches or more across, roughly the size of a cantaloupe, likely strike the United States far more often than official storm records indicate. A peer-reviewed statistical ...

Abstract: Sparse Bayesian learning (SBL)-based methods for wideband direction of arrival (DOA) estimation have shown impressive performance in terms of high resolution. It generally assumes that all ...

ABSTRACT: Variable selection using penalized estimation methods in quantile regression models is an important step in screening for relevant covariates. In this paper, we present a one-step estimation ...

Abstract: Space-time adaptive processing (STAP) is a key technique for suppressing clutter. We develop a unified correlated sparse Bayesian learning (CSBL) framework to improve clutter suppression in ...

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...

Add a description, image, and links to the sparse-bayesian-learning topic page so that developers can more easily learn about it.

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...