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Cross-entropy

In information theory, the cross-entropy between two probability distributionsp{\displaystyle p} and q{\displaystyle q}, over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution q{\displaystyle q}, rather than the true distribution p{\displaystyle p}.

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