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This finding is especially robust because we used entropy balancing to create statistical equivalence.
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Here we used entropy to quantify the contribution of different organizational levels to the overall statistical structure of language.
To estimate image entropy, we use entropy estimation algorithm described in [14, 15].
We use entropy as a measure for image quality.
We use entropy to measure the amount of information.
We use entropy = min all codings − ∑ (i / l ) log 2 (i / l ).
This implied that the used entropy measure captures significant structural information because we know that rooted trees and generalized trees are different graph classes.
To quantify information generation, we used multiscale entropy (MSE) analysis (Costa et al. 2002, 2005).
Entropy-based measures are available that may serve to quantify this, and we used sample entropy [41] that measures the relative "disorder" of a time series.
We used maximum entropy modelling (Maxent; [23], [24]), which is currently regarded as one of the best methods available [25].
We used relative entropy as a tool to characterise the use of nucleotides that flank substitutions.
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