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Found 2 papers, 2 papers with code
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.
Proximal nested sampling with data-driven priors for physical scientists
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.
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