This paper develops a new empirical Bayesian inference algorithm for solving a linear inverse problem given multiple measurement vectors of noisy observable data. Specifically, by exploiting the joint sparsity across the multiple measurements in the sparse domain of the underlying signal or image, we construct a new support informed prior. Several applications can be modeled using this framework, including synthetic aperture radar observations using nearby azimuth angles and parallel magnetic resonance imaging. Our numerical experiments suggest that using the support informed prior usually improves accuracy of the recovery in the form of the sampled posterior mean and reduces its uncertainty when compared to posteriors constructed using some more standard priors.


  1. empirical Bayesian inference
  2. uncertainty quantification
  3. sparsity promoting priors
  4. multiple measurement vectors
  5. joint sparsity

MSC codes

  1. 62C12
  2. 65C40
  3. 68U10

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Information & Authors


Published In

cover image SIAM/ASA Journal on Uncertainty Quantification
SIAM/ASA Journal on Uncertainty Quantification
Pages: 745 - 774
ISSN (online): 2166-2525


Submitted: 29 March 2021
Accepted: 22 February 2022
Published online: 29 June 2022


  1. empirical Bayesian inference
  2. uncertainty quantification
  3. sparsity promoting priors
  4. multiple measurement vectors
  5. joint sparsity

MSC codes

  1. 62C12
  2. 65C40
  3. 68U10



Jiahui Zhang
Department of Mathematics, University of Minnesota, Minneapolis, MN 55455 USA ([email protected]).
Department of Mathematics, Dartmouth College, Hanover, NH 03755 USA ([email protected]).
Theresa Scarnati
Qualis Corporation, Huntsville, AL 35806 USA ([email protected]).

Funding Information

National Science Foundation (NSF): DMS-1502640, DMS-1912685
The work of the second author was partially supported by NSF grants DMS-1502640 and DMS-1912685, AFOSR grant FA9550-18-1-0316, and ONR MURI grant N00014-20-1-2595. The work of the third author was partially supported by AFOSR LRIR 18RYCOR011.

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