Maximum entropy priors with derived parameters in a specified distribution
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We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
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