期刊
ENTROPY
卷 21, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/e21080720
关键词
information measures; empirical estimators; entropy; relative entropy; mutual information; universal estimation
资金
- US National Science Foundation [CCF-1016625]
- Center for Science of Information, an NSF Science and Technology Center [CCF-0939370]
We give a brief survey of the literature on the empirical estimation of entropy, differential entropy, relative entropy, mutual information and related information measures. While those quantities are of central importance in information theory, universal algorithms for their estimation are increasingly important in data science, machine learning, biology, neuroscience, economics, language, and other experimental sciences.
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