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In both cases, the datasets are cross-sectional.

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In both cases, the different datasets have to be normalized appropriately before they can be integrated.

More surprising are the disordered cores in 1X6P of PAK pilin (Dunlop et al., 2005) and 3K0N of the enzyme proline isomerase (Fraser et al., 2009); in both these cases, the datasets were recorded at ambient temperatures.

In all cases, the datasets were too large for a cost-sensitive Random Forest to be run.

In all cases, the datasets comprised n values, xi (i = 1, 2,.., n), generated as described below.

In both cases (datasets A and B), the size of the confidence intervals is larger for the normal tissues analyzed than for their cancerous counterparts.

In both cases, our geochemical datasets suggest that the overall composition of the BFR is controlled by fluid-rock interactions at temperatures higher than 350°C.

While in both cases datasets showing convergence have much stronger trends than those showing divergence, a simple explanation for this disparity is not readily available.

The performance measures have been calculated using all the cases of the dataset (740 cases).

Finally, we have illustrated our Bayesian reference analysis methodology with two real world applications that highlight the flexibility of the exponential power distribution to accommodate both cases when there are outliers in the dataset and also cases when the errors follow a platykurtic distribution.

In this case, the first dataset shows a better accuracy value than the second dataset.

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