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One-Shot Inference in Markov Random Fields
2019
Conference on Uncertainty in Artificial Intelligence
Statistical inference in Markov random fields (MRFs) is NP-hard in all but the simplest cases. As a result, many algorithms, particularly in the case of discrete random variables, have been developed to perform approximate inference. However, most of these methods scale poorly, cannot be applied to continuous random variables, or are too slow to be used in situations that call for repeated statistical inference on the same model. In this work, we propose a novel variational inference strategy
dblp:conf/uai/XiongGYR19
fatcat:3i6htvzx7rhfnkevc3exzilkwq