Multiplicative noise in Bayesian inverse problems: Well-posedness and consistency of MAP estimators
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Multiplicative noise arises in inverse problems when, for example, uncertainty on measurements is proportional to the size of the measurement itself. The likelihood that arises is hence more complicated than that from additive noise. We consider two multiplicative noise models: purely multiplicative noise, and a mixture of multiplicative noise and additive noise. Adopting a Bayesian approach, we provide conditions for the resulting posterior distributions on Banach space to be continuous with respect to perturbations in the data; the inclusion of additive noise in addition to multiplicative noise acts as a form of regularization, allowing for milder conditions on the forward map. Additionally, we show that MAP estimators exist for both the purely multiplicative and mixed noise models when a Gaussian prior is employed, and for the latter prove that they are consistent in the small noise limit when all noise is Gaussian.
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