广义次模信息测度的独立性
Independence Properties of Generalized Submodular Information Measures
摘要：
最近定义了一类由次模函数参数化的项集上的广义信息测度。在本文中，我们提出并研究了各种独立的集合之间的信息措施，其连接的概念。由于熵也可以用来参数化这些措施，我们得出有趣的独立性的随机变量集的熵。我们还研究了多集独立性的概念及其性质。最后，我们提出了优化算法，以获得一个集，是独立于另一个给定的集，也讨论了组合独立性的影响和应用。
英文摘要：
Recently a class of generalized information measures was defined on sets of items parametrized by submodular functions. In this paper, we propose and study various notions of independence between sets with respect to such information measures, and connections thereof. Since entropy can also be used to parametrize such measures, we derive interesting independence properties for the entropy of sets of random variables. We also study the notion of multi-set independence and its properties. Finally, we present optimization algorithms for obtaining a set that is independent of another given set, and also discuss the implications and applications of combinatorial independence.
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。
