Numerical comparisons between Bayesian and frequentist low-rank matrix completion: estimation accuracy and uncertainty quantification

@article{Mai2021NumericalCB,
  title={Numerical comparisons between Bayesian and frequentist low-rank matrix completion: estimation accuracy and uncertainty quantification},
  author={T. T. Mai},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.11749}
}
  • T. T. Mai
  • Published 22 March 2021
  • Computer Science, Mathematics
  • ArXiv
In this paper we perform numerous numerical studies for the problem of low-rank matrix completion. We compare the Bayesian approaches and a recently introduced de-biased estimator which provides a useful way to build confidence intervals of interest. From a theoretical viewpoint, the de-biased estimator comes with a sharp minimax-optimal rate of estimation error whereas the Bayesian approach reaches this rate with an additional logarithmic factor. Our simulation studies show originally… Expand

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