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Differential Entropy

Module by: Anders Gjendemsjø. E-mail the author

Summary: In this module we consider differential entropy.

Consider the entropy of continuous random variables. Whereas the (normal) entropy is the entropy of a discrete random variable, the differential entropy is the entropy of a continuous random variable.

Differential Entropy

Definition 1: Differential entropy
The differential entropy hXhX of a continuous random variable XX with a pdf fxfx is defined as
hX=fxlogfxdx h X x fx fx
Usually the logarithm is taken to be base 2, so that the unit of the differential entropy is bits/symbol. Note that is the discrete case, hXhX depends only on the pdf of XX. Finally, we note that the differential entropy is the expected value of logfx f x , i.e.,
hX=Elogfx h X E f x

Now, consider a calculating the differential entropy of some random variables.

Example 1

Consider a uniformly distributed random variable XX from cc to c+Δ c Δ . Then its density is 1Δ 1 Δ from cc to c+Δ c Δ , and zero otherwise.

We can then find its differential entropy as follows,

hX=cc+Δ1Δlog1Δdx=logΔ h X x c cΔ 1Δ 1Δ Δ
Note that by making ΔΔ arbitrarily small, the differential entropy can be made arbitrarily negative, while taking ΔΔ arbitrarily large, the differential entropy becomes arbitrarily positive.

Example 2

Consider a normal distributed random variable XX, with mean mm and variance σ2σ2. Then its density is 12πσ2exm22σ2 1 2 σ 2 e xm 2 2 σ2 .

We can then find its differential entropy as follows, first calculate logfxfx:

logfx=12log(2πσ2)+logexm22σ2 fx 1 2 2 σ 2 e xm 2 2 σ2
Then since EXm2=σ2 E X m 2 σ 2 , we have
hX=12log(2πσ2)+12loge=12log(2πeσ2) h X 1 2 2 σ 2 1 2 e 1 2 2 e σ 2

Properties of the differential entropy

In the section we list some properties of the differential entropy.

  • The differential entropy can be negative
  • hX+c=hX h X c h X , that is translation does not change the differential entropy.
  • haX=hX+log|a| h a X h X a , that is scaling does change the differential entropy.
The first property is seen from both Example 1 and Example 2. The two latter can be shown by using Equation 1.

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