Once we have trained a model and recovered the marginal posterior densities, we will probably want to use our model for predicting/scoring new samples. This is not as easy as in the classical approach, because our parameters are no longer fixed values, but distributions. This means that the predictions won't be point estimates/values, but a range of possible values, each one of them with an associated probability.