论文标题
关于相对熵与$χ^2 $ divergence之间的关系，概括和应用
On Relations Between the Relative entropy and $χ^2$-Divergence, Generalizations and Applications
论文作者
论文摘要
相对熵和卡方差异是信息理论和统计数据中的基本差异措施。本文的重点是研究两个差异之间的整体关系，这些关系的含义，它们的信息理论应用以及与$ f $ divergences的丰富类别有关的一些概括。本文研究的应用是指无损压缩，类型和较大的偏差方法，强大的数据处理不平等，收缩系数和最大相关性的界限以及与离散时间Markov链类型的平稳性的收敛率。
The relative entropy and chi-squared divergence are fundamental divergence measures in information theory and statistics. This paper is focused on a study of integral relations between the two divergences, the implications of these relations, their information-theoretic applications, and some generalizations pertaining to the rich class of $f$-divergences. Applications that are studied in this paper refer to lossless compression, the method of types and large deviations, strong~data-processing inequalities, bounds on contraction coefficients and maximal correlation, and the convergence rate to stationarity of a type of discrete-time Markov chains.
