Beyond the Bakushinkii veto: regularising linear inverse problems without knowing the noise distribution
Bastian Harrach, Tim Jahn, Roland Potthast
Abstract
Abstract This article deals with the solution of linear ill-posed equations in Hilbert spaces. Often, one only has a corrupted measurement of the right hand side at hand and the Bakushinskii veto tells us, that we are not able to solve the equation if we do not know the noise level. But in applications it is ad hoc unrealistic to know the error of a measurement. In practice, the error of a measurement may often be estimated through averaging of multiple measurements. We integrated that in our anlaysis and obtained convergence to the true solution, with the only assumption that the measurements are unbiased, independent and identically distributed according to an unknown distribution.
Topics & Concepts
MathematicsIndependent and identically distributed random variablesVetoDistribution (mathematics)Convergence (economics)Applied mathematicsInverseNoise (video)Hilbert spaceInverse problemMathematical analysisStatisticsComputer scienceLawArtificial intelligenceRandom variablePolitical scienceImage (mathematics)GeometryEconomicsPoliticsEconomic growthNumerical methods in inverse problemsStatistical and numerical algorithmsImage and Signal Denoising Methods