# How to update weights in RBM (Restricted Boltzmann Machines)?

Related Question: Learning weights in a Boltzmann Machine

I'm trying to understand RBMs and how they are applied in training of Deep Architecture. Being new to the field of statistics, I stumbled upon various notations.

Question 1: What does this symbol represent: <$s_is_j$>

It is said while updating weights of RBMs that, the binary states of each node in hidden unit is set to 1 with a probability p equal to a function (a sigmoid function with the input: the sum of product of visible vector and corresponding weights vector connecting a hidden node).

Question 2: Why is every hidden unit being set to 1 (with probability p) and what is the function of the probability p?

Question 3: As far as I understand weights are updated once the system goes to equilibrium, so when is the system said to be in equilibrium?

Also is there any step-by-step procedures for training a network using RBMs

"Why is every hidden unit being set to 1 ", I think there is no sense if hidden($H_j$) unit set $0$, because no matter what the $V_i$ and $W_{ij}$ set to be (assumption $V_i$ connected $H_j$ by parameter $W_{ij}$), the result is $V_i*W_{ij}*H_j=0$ (in the formula $E(V,H)$ used in energy model); so we hope it to be $1$.
It's not important to know the time when is the system said to be in equilibrium, equilibrium occurs when $P(H(i)|V(i))= P(H(i+L)|V(i+L))$.