Convergence Rates for Linear Inverse Problems in the Presence of an Additive Normal Noise

In this work, we examine a finite-dimensional linear inverse problem where the measurements are disturbed by an additive normal noise. The problem is solved both in the frequentist and in the Bayesian frameworks. Convergence of the used methods when the noise tends to zero is studied in the Ky Fan metric. The obtained convergence rate results and parameter choice rules are of a similar structure for both approaches.