Ways to combine Gibbs sampling with learning
	• The obvious method is to start with a random hidden configuration for each datavector and to do Gibbs sampling until we have reached equilibrium. • Then use the equilibrium samples from the posterior distribution over hidden configurations to update the weights (online or batch or mini-batch) • But how do we decide how much Gibbs sampling is required to reach equilibrium? – There is no simple test and if we don’t do enough there is no guarantee that the learning will work, even if we use an infinitesimal learning rate.