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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-theor...
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| Veröffentlicht in: | Entropy (Basel) |
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| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI
2018
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7512642/ https://ncbi.nlm.nih.gov/pubmed/33265171 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e20020051 |
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