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The entropic vector or entropic function is a concept arising in information theory. It represents the possible values of Shannon's information entropy that subsets of one set of random variables may take. Understanding which vectors are entropic is a way to represent all possible inequalities between entropies of various subsets. For example, for any…
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displaystyle inequalities entropic variables gamma vectors information random entropy set shannon-type inequality dots vector linear tuple overline subsets complexity example
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| conditional information | instance of | Other information-theoretic measures | 0.80 | text |
| mutual information | instance of | Other information-theoretic measures | 0.80 | text |
| or total correlation can be expressed in terms of joint entropy | instance of | Other information-theoretic measures | 0.80 | text |
| are thus related by the corresponding inequalities | instance of | Other information-theoretic measures | 0.80 | text |
| Entropic vector | related to Definition | Shannon's | 0.60 | section |
| Entropic vector | related to Definition | For | 0.60 | section |
| Entropic vector | related to Definition | Here | 0.60 | section |
| Entropic vector | related to Kolmogorov complexity | Kolmogorov | 0.60 | section |
| Entropic vector | related to Kolmogorov complexity | Namely | 0.60 | section |
| Entropic vector | related to Kolmogorov complexity | The | 0.60 | section |
| Entropic vector | related to Kolmogorov complexity | Similarly | 0.60 | section |
| Entropic vector | related to Kolmogorov complexity | Andrey Kolmogorov | 0.60 | section |
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