This tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The information measure known as information divergence or Kullback-Leibler distance or relative entropy plays a key role, often with a geometric flavor as an analogue of squared Euclidean distance, as in the concepts of I-projection, I-radius and I-centroid. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an “information geometry” background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory.

信息论与编码（曹雪红） 课后答案

信息论基础教程习题解答与上机知道 李梅李亦农 1-8章答案

信息论基础教程 第六章 (李亦农 李梅 著) 北京邮电大学出版社，文件为pdf，内容比较清晰。

信息论基础 中文版

信息论与编码课后题的答案 信息论与编码(第2版) 陈运 (编者)

\李亦农信息论基础教程课后习题答案(2—4)\信息论基础教程 第3章 (李亦农 李梅 著)\李亦农信息论基础教程课后习题答案(2—4)\信息论基础教程 第3章 (李亦农 李梅 著)

信息论与编码课后习题答案 第二版陈运主编

信息论与编码试卷及答案，对学习信息论的同学是很有帮助的

信息论与编码 曹雪虹 张宗橙 清华大学出版社 习题答案 课后题答案，比较全面的