Abstract
A generalized maximum-entropy-based approach to noisy inverse problems such as the Abel problem, tomography or deconvolution is presented. In this generalized method, instead of employing a regularization parameter, each unknown parameter is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of both, the noise and signal probabilities, are maximized subject to the observed data. After developing the method, information measures, basic statistics and the covariance structure are developed as well. This method is contrasted with other approaches and includes the classical maximum-entropy formulation as a special case. The method is then applied to the tomographic reconstruction of the soft x-ray emissivity of the hot fusion plasma.
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More From: Journal of Physics A: Mathematical and General
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