Context: I am implementing Gaussian Bernoulli RBM, it is like the popular RBM but with real-valued visible units. True that the procedure of sampling hidden values p(h=1|v) are the same for both, i.e. Problem: My problem is in coding (using Python) p(v|h) , which is, I am a little bit confused as to how N() works. Do I simply add Gaussian noise using the data's standard deviation to b + sigma * W.dot(h) ? Thank you in advance.

(Python) Gaussian Bernoulli RBM on computing P(v|h)

Context:

I am implementing Gaussian Bernoulli RBM, it is like the popular RBM but with real-valued visible units.

True that the procedure of sampling hidden values p(h=1|v) are the same for both, i.e.

enter image description here

Problem:

My problem is in coding (using Python) p(v|h), which is,

enter image description here

I am a little bit confused as to how N() works. Do I simply add Gaussian noise using the data's standard deviation to b + sigma * W.dot(h)?

Thank you in advance.