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arXiv 2102.08157cs.ITmath.IT
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Wyner公共信息的下界

Lower bound on Wyner's Common Information

Erixhen Sula, Michael Gastpar

摘要:

两个随机变量之间的公共信息的一个重要概念是由于Wyner。本文给出了连续型随机变量的Wyner公共信息的一个下界。新的界限改进了Wyner的公共信息,这是互信息的唯一的另一个一般的下界。我们还表明,新的下界是紧的所谓的“高斯通道”的情况下,即,当随机变量的联合分布可以写为一个单一的潜在的随机变量和高斯噪声的总和。我们激励这项工作从最近的变化Wyner的共同信息和应用网络数据压缩的问题,如灰色Wyner网络。

英文摘要:

An important notion of common information between two random variables is due to Wyner. In this paper, we derive a lower bound on Wyner's common information for continuous random variables. The new bound improves on the only other general lower bound on Wyner's common information, which is the mutual information. We also show that the new lower bound is tight for the so-called "Gaussian channels" case, namely, when the joint distribution of the random variables can be written as the sum of a single underlying random variable and Gaussian noises. We motivate this work from the recent variations of Wyner's common information and applications to network data compression problems such as the Gray-Wyner network.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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