对称发散测度间的不等式及其加细
Inequalities Among Symmetric Divergence Measures and Their Refinement
摘要：
在信息论和统计学的文献中有三种经典的散度度量，即Jeffryes-Kullback-Leiber的J-散度，Sibson-Burbea-Rao的Jensen-Shannon散度和Taneja的算术-几何平均散度。它们之间有着有趣的关系，并且基于对数表达式。像Hellinger歧视，对称卡方发散和三角歧视的分歧措施不是基于对数表达式。这六个发散度量相对于概率分布是对称的。本文研究了这些对称散度测度之间的一些有趣的不等式。对这些不等式也进行了改进。对Dragomir等人的一些不等式也作了改进。
英文摘要：
There are three classical divergence measures in the literature on information theory and statistics, namely, Jeffryes-Kullback-Leiber's J-divergence, Sibson-Burbea-Rao's Jensen-Shannon divegernce and Taneja's arithemtic-geometric mean divergence. These bear an interesting relationship among each other and are based on logarithmic expressions. The divergence measures like Hellinger discrimination, symmetric chi-square divergence, and triangular discrimination are not based on logarithmic expressions. These six divergence measures are symmetric with respect to probability distributions. In this paper some interesting inequalities among these symmetric divergence measures are studied. Refinement over these inequalities is also given. Some inequalities due to Dragomir et al. are also improved.
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。
