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there already exist methods to generate skewed distributions that are very similar to the ones shown in the article. not sure how this is new. Skewed distributions arise form a more general case of the normal distribution but with added parameters or the use of special functions

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The main point is not about generating skewed distributions, it's about "universality" - the same distribution starts popping up in seemingly unrelated settings. E.g., (large) eigenvalues of random matrices is connected to the length of the longest increasing subsequence of permutations. In a sense it's the discovery of a new kind of central limit theorem.

As someone who has studied statistics in college, there is little original or novel about these finding. Any distribution can be reversed-engineered to find its characteristic function.

Yeah but certain characteristic functions popping up in seemingly unrelated places is a BIG deal.

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