I’m constructing a hierarchical GLM (thanks to the great examples in the notebook folder) but I’m not sure if I’m predicting the out-of-sample data correctly.
The radon notebook takes the trace of the linear regression parameters and uses the mean of those to build a predictive model (see the plots comparing different districts).
On the other hand, the Bayesian NN notebook uses the
sample_ppc function to generate predictions and takes then averages over those predictions (see the first
As I understand, the second approach is more rigorous as it takes account of the uncertainty in the model parameters. However, I’m not completely sure and willing to be educated on this. Perhaps these actually achieve slightly different objectives?