are quite easy to to extend to this Bayesian setting so what we can do is we can use amortized variational inference we can train a neural network to represent a distribution over sufficient statistics we can also represent a distribution over full parameter vectors Phi I although that might get a bit unwieldy at some point and so we could train a neural network to represent a Gaussian distribution over H and then feed this H into another neural network and use ideas from from amortized variational inference such as the