Cross-entropy
In information theory, given two probability distributions, the average number of bits needed to identify an event if the coding scheme is optimized for the ‘wrong’ probability distribution rather than the true distribution
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Cross-entropy
In information theory, given two probability distributions, the average number of bits needed to identify an event if the coding scheme is optimized for the ‘wrong’ probability distribution rather than the true distribution
In information theory, the cross-entropy between two probability distributions p {\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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From Wikipedia
In information theory, the cross-entropy between two probability distributions p {\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} .
Text: Wikipédia, CC BY-SA 4.0. ·
