Software implementations of DBMs need to have many different components for CD training of individual RBMs, PCD training of the full DBM, and training based on back-propagation through the MLP. The MLP on top of the Boltzmann machine loses many of the advantages of the Boltzmann machine probabilistic model, such as being able to perform inference when some input values are missing.

There are two main ways to resolve the joint training problem of the deep Boltzmann machine, centered deep Boltzmann machine and multi-prediction deep Boltzmann machine