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Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques
The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities i...
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| 出版年: | Entropy (Basel) |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7516551/ https://ncbi.nlm.nih.gov/pubmed/33285916 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e22020141 |
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