2023/03/29 · We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors.

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

2024/05/28 · We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors.

2024/05/24 · Our findings show that incorporating joint sparsity into the current hierarchical Bayesian methodology can significantly improve its performance ...

2023/03/31 · We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors.

J. Glaubitz, A. Gelb. Leveraging joint sparsity in hierarchical Bayesian learning. Accepted for publication, SIAM-ASA J Uncertain Quantif (2024).

This investigation extends these ideas by introducing a Bayesian volumetric approach that leverages the assumption of sequential joint sparsity.

2021/03/29 · This paper develops a new empirical Bayesian inference algorithm for solving a linear inverse problem given multiple measurement vectors ...

2021/03/29 · This paper develops a new empirical Bayesian inference algorithm for solving a linear inverse problem given multiple measurement vectors ...

Bayesian learning with Gaussian processes demonstrates encouraging regression and classification performances in solving computer vision tasks.