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November 3

Entropy of uniform distribution

Melbourne, Australia

Looking at the entries for uniform distribution the entropy is given as ln(n) for the discrete case, and ln(b-a) for the continuous case. For a normalised distribution in the interval [0,1] the discrete case agrees with Shannon's definition of entropy, but the continuous case produces entropy of 0. Is this correct? Rjeges (talk) 10:06, 3 November 2013 (UTC)[reply]

I think it would be more accurate to say that the entropy in the continuous case is undefined. Looie496 (talk) 14:30, 3 November 2013 (UTC)[reply]

From Differential entropy#Definition:

Let X be a random variable with a probability density function f whose support is a set $\mathbb {X}$ . The differential entropy h(X) or h(f) is defined as

h(X)=-\int _{\mathbb {X} }f(x)\log f(x)\,dx

.

and

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

\int _{0}^{\frac {1}{2}}-2\log(2)\,dx=-\log(2)\,

.

Thus, differential entropy does not share all properties of discrete entropy.

Duoduoduo (talk) 15:58, 3 November 2013 (UTC)[reply]