基于预测信息的统计复杂性度量
A measure of statistical complexity based on predictive information
摘要：
我们介绍了一个信息理论的统计结构，称为“绑定信息”，为随机变量集的措施，并比较它与以前提出的措施，包括过剩熵，Bialek等人。的预测信息和多元信息。我们推导出的绑定信息的一些属性，特别是在有关的多信息，并表明，对于有限组的二进制随机变量，最大化绑定信息的过程是“奇偶校验”的过程。最后，我们讨论了一些的影响，这对于使用的绑定信息的复杂性的措施。
英文摘要：
We introduce an information theoretic measure of statistical structure, called 'binding information', for sets of random variables, and compare it with several previously proposed measures including excess entropy, Bialek et al.'s predictive information, and the multi-information. We derive some of the properties of the binding information, particularly in relation to the multi-information, and show that, for finite sets of binary random variables, the processes which maximises binding information are the 'parity' processes. Finally we discuss some of the implications this has for the use of the binding information as a measure of complexity.
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