To make use of the expression of the negative phase as an expectation we can use Monte Carlo methods to approximately maximise the likelihood of models with intractible partition functions. The naive way of implemnting this would be to burn in a set of Markov chains from a random initialization every time the gradient is needed. However, this approach is infeasible when learning using stochastic gradient descent as this means the chains must be burned in once per gradient step.