I might be mistaken, but I think this is partly because of the undirected structure of RBMs, so you can't build a computational graph in the same way as with feed-forward networks.
Edit: Actually was talking about the General Boltzmann Machine. For the Restricted Boltzmann Machine an approximation has been assumed which obviates the need for full Gibbs sampling during training. Then (quoting the article, emphasis mine) "after training, it can sample new data from the learned distribution using Gibbs sampling."