The two do roughly the same thing. In the radon notebook we recreate the model again outside of PyMC3 (simple since it’s just linear) and draw lines given the trace. Note that we could just re-use the existing model specification by using theano.shared variables and change the data before sample_ppc to evaluate over a grid, like the Bayesian NN example does. I think the Bayesian NN approach should be preferred as it’s harder to shoot yourself in the foot.