Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems
نویسندگان
چکیده
منابع مشابه
Minimising the Kullback-Leibler Divergence for Model Selection in Distributed Nonlinear Systems
The Kullback–Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are...
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ژورنال
عنوان ژورنال: Entropy
سال: 2018
ISSN: 1099-4300
DOI: 10.3390/e20020051