Feb 24, 2021 · This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting ...

Abstract. We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses ...

This work aims to study variational Bayesian inference for sparse regression. Sparsity promoting priors have proven to be very successful in regression ...

It is shown that the performance in terms of variable selection and uncertainty quantification of the variational Bayesian LASSO can be comparable to the ...

This work considers variational Bayesian inference as an inexpensive andscalable alternative to a fully Bayesian approach in the context ...

Sparse Online Variational Bayesian Regression. Open Webpage · Kody J. H. Law, Vitaly Zankin. Published: 31 Dec 2021, Last Modified: 13 May 2024SIAM/ASA J ...

Oct 22, 2024 · Request PDF | On Sep 16, 2022, Kody J. H. Law and others published Sparse Online Variational Bayesian Regression | Find, read and cite all the

This repository contains the code implementation of the Sparse Online Variational Bayesian Regression paper as well as two motivational demos.

We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression.

We introduce an online variational Bayesian model for tracking changes in a non-stationary, multivariate, temporal signal.