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Abstract:
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We propose a novel, lucid class of probability distributions for approximating an arbitrary distribution with a bounded support. We explain the mathematical and practical motivations for the proposal. We also study how to apply fitting algorithms, such as the EM algorithm, to estimate from sample data the best approximating distribution(s) within this class. Finally, we present numerical results based on fits to both theoretically given distributions and those to real data.
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