Information Theory
In information theory and statistics, negentropy is used as a measure of distance to normality. Out of all distributions with a given variance, the normal or Gaussian distribution is the one with the highest entropy. Negentropy measures the difference in entropy between a given distribution and the Gaussian distribution with the same variance. Thus, negentropy is always nonnegative, is invariant by any linear invertible change of coordinates, and vanishes if and only if the signal is Gaussian.
Negentropy is defined as
where is the differential entropy of the Gaussian density with the same mean and variance as and is the differential entropy of :
Negentropy is used in statistics and signal processing. It is related to network entropy, which is used in Independent Component Analysis.
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