Test results for the final data-vector’s Gaussianity. For each of the 180 data vector entries, we take the set of 10 × 124 measurements in the Covariance Training Sample and test the null hypothesis that this sample was drawn from a normal distribution using the measured skewness and kurtosis (D’Agostino 1971; D’Agostino & Pearson 1973). We then plot a histogram of the corresponding p-values (blue). If each entry of the data vector is Gaussian, then the distribution of p-values is uniform. For comparison, we show the results of the same test with a data vector that contains only points with about 10 features (orange). While the blue histogram may show small deviations from a uniform distribution (there appears to be a downward slope towards higher p-values), we believe that the assumption of a normal distribution is reasonable.