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maximum entropy or maxent is a statistical principle derived from information theory. Distributions maximizing entropy (under some constraints) are thought to be "maximally uninformative" given the constraints. Maximum entropy can be used for multiple purposes, like choice of prior, choice of sampling model, or design of experiments.

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Proving that Shannon entropy is maximised for the uniform distribution

I know that Shannon entropy is defined as $-\sum_{i=1}^kp_i\log(p_i)$. For the uniform distribution, $p_i=\frac{1}{k}$, so this becomes $-\sum_{i=1}^k\frac{1}{k}\log\left(\frac{1}{k}\right)$. Further …
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