Constraint-aware confidence intervals for ill-posed inverse problems are made computationally practical via a Berger-Boos bounding set, sampling, and quantile regression, achieving nominal coverage with shorter intervals than OSB.
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Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration
Constraint-aware confidence intervals for ill-posed inverse problems are made computationally practical via a Berger-Boos bounding set, sampling, and quantile regression, achieving nominal coverage with shorter intervals than OSB.