Previously, this was done by looking at the average of the predictions
over the posterior predictive. This was removed, as this didn't make
much sense.

Several metrics were included in evaluate_tabular.py. To this end
the file coverage_metrices.py was added and the processing of a larger
number of metrics in evaluate_tabular.py was simplified.

The evaluate_tabular script was simplified to ease the analysis of more
quantities. Several coverage quantities and the bias were added to these
quantities. However, they perform rather poor.

Added EiV training scripts for the three datasets and moreover included
bias evaluation in `evaluate_tabular`. The inclusion of some coverage
measure is still needed.

This covers all regression datasets treated in the MC Dropout and Deep
Ensemble paper. Results are comparable or even better. For multivariate
dataset, the decouple_dimensions keyword in the evaluation scripts can
be used to follow the (rather weird) convention of these papers.

Added training and testing for nonEiV-model on the power plant dataset.
The evaluation script for MSD was also added.

For NLL metric, weird convention from MC Dropout was integrated.
Power Plant example missing.

Both reaching similar RMSEs as in MC Dropout paper.
Updated hidden_layers handling in all training files.