期望测度的信息论
Information Theory for Expectation Measures
AI总结：
本文提出基于期望测度的信息论，研究无随机性数据集、点过程及变样本量抽样问题，并结合Kraft不等式阐明概率测度量化随机性的适用条件。
摘要：
Shannon基于Kolmogorov发展的概率测度概念建立了信息论。本文基于期望测度研究信息论中的一些基本问题。在期望测度理论中，研究无随机性的数据集是很自然的，同样自然的是研究点过程的信息论以及样本量不固定的抽样。期望测度结合Kraft不等式可用于阐明在哪些情况下可以使用概率测度来量化随机性。
英文摘要：
Shannon based his information theory on the notion of probability measures as it we developed by Kolmogorov. In this paper we study some fundamental problems in information theory based on expectation measures. In the theory of expectation measures it is natural to study data sets where no randomness is present and it is also natural to study information theory for point processes as well as sampling where the sample size is not fixed. Expectation measures in combination with Kraft's Inequality can be used to clarify in which cases probability measures can be used to quantify randomness.
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。
