The Entropic Central Limit Theorem for Discrete Random Variables

Lampros Gavalakis, I. Kontoyiannis
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Abstract

An information-theoretic proof of a strengthened version of the classical discrete central limit theorem is presented. Using only information-theoretic and elementary arguments, convergence to zero of the relative entropy between the standardised sum of n independent and identically distributed lattice random variables and an appropriately discretised Gaussian is established.
离散随机变量的熵中心极限定理
给出了经典离散中心极限定理的一个强化版的信息论证明。仅利用信息论和初等论证,建立了n个独立同分布格随机变量的标准化和与一个适当离散高斯变量的相对熵收敛于零的定理。
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