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- W3208024905 abstract "Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models." @default.
- W3208024905 created "2021-11-08" @default.
- W3208024905 creator A5041990954 @default.
- W3208024905 date "2021-11-03" @default.
- W3208024905 modified "2023-10-10" @default.
- W3208024905 title "Regularization, Bayesian Inference and Machine Learning methods for Inverse Problems†" @default.
- W3208024905 doi "https://doi.org/10.20944/preprints202111.0092.v1" @default.
- W3208024905 hasPublicationYear "2021" @default.
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