Mohammad-Djafari, A. Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems. Entropy2021, 23, 1673.
Mohammad-Djafari, A. Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems. Entropy 2021, 23, 1673.
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.
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