Bayes risk a-optimal experimental design methods for ill-posed inverse problems
Optimal experimental design methods are well established and have been applied in a variety of different fields. Most of the classical methods in optimal experimental design however neglect the subject of ill-posedness. Ill-posedness is an issue that is prevalent when solving inverse problems. In order to solve most real-world inverse problems of interest, we must use methods known as regularization to help stabilize the final solution. The use of regularization introduces a bias into the obtained estimates that classical optimal experimental design techniques do not take into account. The primary ...
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