where E indicates expectation value, µ X is the mean of random variable X, µ Y is the mean of random variable Y , σ X is the standard deviation of X and σ Y is the standard deviation of Y . The Pearson correlation defines the best linear fit between data sets sampled from X and Y . As mentioned before, the Pearson correlation suffers a number of insufficiencies when used for data that is related nonlinearly, and sets of related vector data that oscillate in non-parallel directions. The MI can address these shortcomings. In order to provide an equivalent quantity to r in eq. 11, researchers have defined the GC coefficient r MI , which makes use of the MI.