Editing Boltzmann machine (section)

==Equilibrium state==
The network runs by repeatedly choosing a unit and resetting its state. After running for long enough at a certain temperature, the probability of a global state of the network depends only upon that global state's energy, according to a [[Boltzmann distribution]], and not on the initial state from which the process was started. This means that log-probabilities of global states become linear in their energies. This relationship is true when the machine is "at [[thermal equilibrium]]", meaning that the probability distribution of global states has converged. Running the network beginning from a high temperature, its temperature gradually decreases until reaching a [[thermal equilibrium]] at a lower temperature. It then may converge to a distribution where the energy level fluctuates around the global minimum. This process is called [[simulated annealing]].

To train the network so that the chance it will converge to a global state according to an external distribution over these states, the weights must be set so that the global states with the highest probabilities get the lowest energies. This is done by training.