Differential entropy (also referred to as continuous entropy) in information theory is a property of absolutely continuous probability distributions which generalizes the Shannon entropy to continuous probability distributions. In terms of measure theory, the differential entropy of a probability measure is the negative relative entropy from that measure to the Lebesgue measure, where the latter is treated as if it were a probability measure, despite being unnormalized.
Differential entropy (also referred to as continuous entropy) in information theory is a property of absolutely continuous probability distributions which generalizes the Shannon entropy to continuous probability distributions. In terms of measure theory, the differential entropy of a probability measure is the negative relative entropy from that measure to the Lebesgue measure, where the latter is treated as if it were a probability measure, despite being unnormalized.
Source: Wikipedia "Differential entropy" · CC BY-SA 4.0
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