On the Partition Function and Random Maximum A-Posteriori Perturbations
  In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function. We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches. 
Tamir Hazan (TTIC), Tommi Jaakkola (MIT)
cs.LG stat.ML