Often, computing marginal estimates of a model is computationally intractable due to the normalization constant Z, while maximum a posteriori (MAP) is prohibitive due to the search space of possible configurations.
instead of evaluating the log-score ψ of the model exactly, this paper proposes a Monte-Carlo estimation of the log-score.