Since the goal is to approximate the data distribution with a model parametrized by θ, the conditional class distribution is defined as P (w|C = 1, H) = P θ (w|H) and P (w|C = 0, H) = P n (w), which gives the posterior class probabilities: and P (C = 0|w, H) = kP n (w) If σ denotes the sigmoid function, these equations can be rewritten as a logistic regression problem: The reformulation obtained in equation 7 shows that training a classifier based on a logistic regression will estimate the log-ratio of two distributions: this allows the learned distribution to be un-normalized, and the partition function to be parametrized separately 3 .
the partition function is context-dependent.