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When Noise Estimation Hides Basis Misspecification in Repeated Bayesian Inverse Problems
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis. We show that estimating the observation-noise variance can hide this loss. Under a linear forward model, when the in-span prior variance dominates the noise, the maximum-likelihood
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