sparse-bayesian-learning

Functional models and algorithms for sparse signal processing

The sparse Bayesian learning sandbox

Sparse Bayesian Metric Learning

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

Hierarchical probabilistic models for multiple gene/variant associations based on NGS data

Official implementation of AMP-based consistent activity status and channel estimation

enhanced GADA, a Fast and Sparse Bayesian Learning-based Segmentation Algorithm

Python library and TRIPS-Py examples for prior-normalizing (Knothe-Rosenblatt) transport maps in sparse Bayesian learning (SBL): MAP demos for 1D/2D deblurring alongside Tikhonov/GCV baselines. Companion to Glaubitz & Marzouk (2025) and the Julia reference implementation.

Official implementation of Bayesian activity status and system parameter estimation

direct position determination of non-circular sources via off-grid sparse Bayesian inference

R Package for Automatic Relevance Determination

Deconvolution algorithms for diffusion MRI

Code repository for the paper "Joint Model and Data Sparsification via the Marginal Likelihood" @ ICML 2026

CL-SCA: covariance learning via successive convex approximations