传递熵
信息传递
计算机科学
熵(时间箭头)
信息论
人工智能
最大熵原理
数学
统计
物理
电信
量子力学
作者
Michael Wibral,Raúl Vicente,Michael Lindner
出处
期刊:Understanding complex systems
日期:2014-01-01
卷期号:: 3-36
被引量:111
标识
DOI:10.1007/978-3-642-54474-3_1
摘要
Information transfer is a key component of information processing, next to information storage and modification. Information transfer can be measured by a variety of directed informationmeasures of which transfer entropy is themost popular, andmost principled one. This chapter presents the basic concepts behind transfer entropy in an intuitive fashion, including graphical depictions of the key concepts. It also includes a special section devoted to the correct interpretation of the measure, especially with respect to concepts of causality. The chapter also provides an overview of estimation techniques for transfer entropy and pointers to popular open source toolboxes. It also introduces recent extensions of transfer entropy that serve to estimate delays involved in information transfer in a network. By touching upon alternative measures of information transfer, such as Massey's directed information transfer and Runge's momentary information transfer, it may serve as a frame of reference for more specialised treatments and as an overview over the field of studies in information transfer in general.
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