describes the entire vector to be generated and then the rest of the process involves translating that vector into something that lies in the data space and that allows us to do things like have embeddings that are useful for semi-supervised learning or generating samples that have particular properties that were interested in fully visible belief networks don't do this out of the box but there are different extensions of them that can enable these abilities one very recent example of a fully visible belief net is wavenet and it