  Following the growing success of generative neural networks in LHC
simulations, the crucial question is how to control the networks and assign
uncertainties to their event output. We show how Bayesian normalizing flow or
invertible networks capture uncertainties from the training and turn them into
an uncertainty on the event weight. Fundamentally, the interplay between
density and uncertainty estimates indicates that these networks learn functions
in analogy to parameter fits rather than binned event counts.
