2 To fit the model, we maximize the log-likelihood of the training data , with respect to the model parameters θ. Optimization is done with gradient-based methods-requiring the computation of log Z(w) and ∇ log Z(w), which is intractable.
3 we turn to sampling (Rubinstein and Kroese, 2011) and stochastic gradient methods.