  Bayesian Flow Networks (BFNs) has been recently proposed as one of the most
promising direction to universal generative modelling, having ability to learn
any of the data type. Their power comes from the expressiveness of neural
networks and Bayesian inference which make them suitable in the context of
continual learning. We delve into the mechanics behind BFNs and conduct the
experiments to empirically verify the generative capabilities on non-stationary
data.
