The 'conventional' configuration of RBMs are Binary-Binary and Gaussian-Binary (and sometimes Binary-Gaussian) units.
Although it is possible for both the visible and hidden units to be gaussian, wouldn't a Gaussian-Gaussian RBM just resemble a linear model, since there is no non-linearity in the networks units anymore? Thus, stacking them would not not have the same benefit as for, say, Binary-Binary RBMs. And when using them for dimensionality reduction, a simple PCA would achieve better results?
Am I missing any significant points in the training of RBMs or are Gaussian-Gaussian RBMs just that limited?