Point estimation update: quantile loss, activation function, simulation-based calibration, polished notebook

[han-ol]

This pull request includes new components for amortized point estimation, namely

• quantile_loss() to simultaneously train multiple quantile levels
• QuantileActivation, a tf.keras Layer to add at the end of a neural architecture for quantile estimation to exploit that quantiles are ordered according to their levels (inductive bias)

It also includes a related API addition, namely

• select the quantile_loss() conveniently by specifying quantile_levels, much like norm_ord does for the norm_diff() loss, e.g. quantile_levels=[0.1, 0.5, 0.9].

Finally, there is an updated tutorial notebook including some theory and showcasing the above + parametric bootstrapping and a simulation-based calibration test.

[han-ol]

Related to https://github.com/stefanradev93/BayesFlow/issues/121

[stefanradev93]

Awesome addition, Hans!