On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Tamir Hazan, Subhransu Maji, Tommi Jaakkola


In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low dimensional perturbations and solving the corresponding MAP assignments. Our approach also leads to new ways to derive lower bounds on partition functions. We demonstrate empirically that our method excels in the typical high signal - high coupling'' regime. The setting results in ragged energy landscapes that are challenging for alternative approaches to sampling and/or lower bounds. "