Definition
Let X be a random variable with a probability density function f whose support is a set . The differential entropy h(X) or h(f) is defined as
- .
As with its discrete analog, the units of differential entropy depend on the base of the logarithm, which is usually 2 (i.e., the units are bits). See logarithmic units for logarithms taken in different bases. Related concepts such as joint, conditional differential entropy, and relative entropy are defined in a similar fashion.
One must take care in trying to apply properties of discrete entropy to differential entropy, since probability density functions can be greater than 1. For example, Uniform(0,1/2) has negative differential entropy
- .
Thus, differential entropy does not share all properties of discrete entropy.
Note that the continuous mutual information I(X;Y) has the distinction of retaining its fundamental significance as a measure of discrete information since it is actually the limit of the discrete mutual information of partitions of X and Y as these partitions become finer and finer. Thus it is invariant under non-linear homeomorphisms (continuous and uniquely invertible maps), including linear transformations of X and Y, and still represents the amount of discrete information that can be transmitted over a channel that admits a continuous space of values.
Read more about this topic: Differential Entropy
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