MEASURING CORRELATIONS IN SYMBOL SEQUENCES

By H. Herzel, Ivo Grosse. 1995

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On the origin of long-range correlations in texts (2012, neutral): “Apart from these applications, more fundamental extensions of our results should: (i) consider the mutual information and similar entropy-related quantities, which have been widely used to quantify long-range correlations (9, 18) [see (38) for a comparison to correlations]; (ii) go beyond the simple”
Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data (2004, background): “As shown in Figure 3 (right), the standard deviation of the mutual information estimated with the simple binning (k = 1) scales with 1/N for statistically independent events [26,29].”
Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors (2009, methodology): “It has also been widely reported that positions sharing high MI with other positions are generally located within functionally important surfaces such as the ligand-binding sites and form a network or clique [31–38,40–47,49–55,57].”
The mutual information: Detecting and evaluating dependencies between variables (2002, methodology): “Since the mutual information, as defined in Equation (10), is a sum of entropies, we may use this expression to estimate the systematic error of I (X, Y) ( Herzel and Grosse, 1995; Grosse, 1996; Roulston, 1999).”