We consider the problem of uncertainty quantification for an unknown low-rank matrix $mathbf{X}$, given a partial and noisy observation of its entries. This quantification of uncertainty is essential for many real-world problems, including image processing, satellite imaging, and seismology, providing a principled framework for validating scientific conclusions and guiding decision-making. However, existing literature has largely focused on the completion (i.e., point estimation) of the matrix $mathbf{X}$, with little work on investigating its uncertainty. To this end, we propose in this work a new Bayesian modeling framework, called BayeSMG, which parametrizes the unknown $mathbf{X}$ via its underlying row and column subspaces. This Bayesian subspace parametrization allows for efficient posterior inference on matrix subspaces, which represents interpretable phenomena in many applications. This can then be leveraged for improved matrix recovery. We demonstrate the effectiveness of BayeSMG over existing Bayesian matrix recovery methods in numerical experiments and a seismic sensor network application.
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