Figure 1.

MI and MI rank estimation errors for varying Gaussian kernel widths. The mean absolute percent error in estimating mutual information for bivariate normal densities is compared to the percent of errors in ranking the relative mutual information values for randomly sampled pairs for which the distribution with the lower true MI value is between 70% and 99% of the distribution with the higher value. MI estimation error (dashed blue line) is highly sensitive to the choice of Gaussian kernel width used by the estimator and grows rapidly for non-optimal parameter choices. However, due to similar bias for distributions with close MI values, the error in ranking pairs of MIs (solid red line) is much less sensitive to the choice of this parameter. These averages were produced using samples from 1,000 bivariate normal densities with a random uniformly distributed correlation coefficient ρ ∊ [0.1, 0.9], such that

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. This results in a distribution of MI values that closely resembles that of the real microarray data.

Margolin et al. BMC Bioinformatics 2006 7(Suppl 1):S7   doi:10.1186/1471-2105-7-S1-S7