Functional models and algorithms for sparse signal processing

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.

The sparse Bayesian learning sandbox

Deconvolution algorithms for diffusion MRI

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

Sparse Bayesian Metric Learning

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

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

R Package for Automatic Relevance Determination

Official implementation of Bayesian activity status and system parameter estimation

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.

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