Dynamic Importance Sampling for Anytime Bounds of the Partition Function

Qi Lou, Rina Dechter, Alexander T. Ihler

Neural Information Processing Systems 

Computing the partition function is a key inference task in many graphical models. In this paper, we propose a dynamic importance sampling scheme that provides anytime finite-sample bounds for the partition function.

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