Entropy power inequality

In mathematics, the entropy power inequality is a result in probability theory that relates to so-called "entropy power" of random variables. It shows that the entropy power of suitably well-behaved random variables is a superadditive function. The entropy power inequality was proved in 1948 by Claude Shannon in his seminal paper "A Mathematical Theory of Communication". Shannon also provided a sufficient condition for equality to hold; Stam (1959) showed that the condition is in fact necessary.

[edit] Statement of the inequality

For a random variable X : Ω → Rⁿ with probability density function f : Rⁿ → R, the information entropy of X, denoted h(X), is defined to be

$h(X) = - \int_{\mathbb{R}^{n}} f(x) \log f(x) \, \mathrm{d} x$

and the entropy power of X, denoted N(X), is defined to be

$N(X) = \frac1{2 \pi e} \exp \left( \frac{2}{n} h(X) \right).$

In particular,N(X) = |K| ^1/n when X ~ Φ_K.

Let X and Y be independent random variables with probability density functions in the L^p space L^p(Rⁿ) for some p > 1. Then

$N(X + Y) \geq N(X) + N(Y). \,$

Moreover, equality holds if and only if X and Y are multivariate normal random variables with proportional covariance matrices.

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Gardner, Richard J. (2002). "The Brunn-Minkowski inequality". Bull. Amer. Math. Soc. (N.S.) 39 (3): 355–405 (electronic). doi:10.1090/S0273-0979-02-00941-2.
Shannon, Claude E. (1948). "A mathematical theory of communication". Bell System Tech. J. 27: 379–423, 623–656.
Stam, A.J. (1959). "Some inequalities satisfied by the quantities of information of Fisher and Shannon". Information and Control 2: 101–112. doi:10.1016/S0019-9958(59)90348-1.