微分熵的一个概率上界
A Probabilistic Upper Bound on Differential Entropy
摘要：
一个新的，非平凡的，概率上界的熵的一个未知的一维分布，给定的分布和样本的支持，从该分布。除了未知分布的支持之外，不需要任何知识，也不需要分布具有密度。在给定随机变量的样本的情况下，该变量的累积分布函数的先前的分布自由界限被用于构造界限。提供了一种简单、快速、直观的计算样本熵界的算法。
英文摘要：
A novel, non-trivial, probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required, nor is the distribution required to have a density. Previous distribution-free bounds on the cumulative distribution function of a random variable given a sample of that variable are used to construct the bound. A simple, fast, and intuitive algorithm for computing the entropy bound from a sample is provided.
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。
