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• W2963261054 abstract "Both for the theoretical and practical treatment of Inverse Problems, the modeling of the noise is crucial. One either models the measurement via a deterministic worst-case error assumption or assumes a certain stochastic behavior of the noise. Although some connections between both models are known, the communities develop rather independently. In this paper we seek to bridge the gap between the deterministic and the stochastic approach and show convergence and convergence rates for Inverse Problems with stochastic noise by lifting the theory established in the deterministic setting into the stochastic one. This opens the wide field of deterministic regularization methods for stochastic problems without having to do an individual stochastic analysis for each problem." @default.
• W2963261054 created "2019-07-30" @default.
• W2963261054 creator A5079317425 @default.
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• W2963261054 date "2017-01-01" @default.
• W2963261054 modified "2023-09-25" @default.
• W2963261054 title "On the lifting of deterministic convergence rates for inverse problems with stochastic noise" @default.
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• W2963261054 doi "https://doi.org/10.3934/ipi.2017031" @default.
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