Lifted Division for Lifted Hugin Belief Propagation
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:6501-6510, 2022.
The lifted junction tree algorithm (LJT) is an inference algorithm that allows for tractable inference regarding domain sizes. To answer multiple queries efficiently, it decomposes a first-order input model into a first-order junction tree. During inference, degrees of belief are propagated through the tree. This propagation significantly contributes to the runtime complexity not just of LJT but of any tree-based inference algorithm. We present a lifted propagation scheme based on the so-called Hugin scheme whose runtime complexity is independent of the degree of the tree. Thereby, lifted Hugin can achieve asymptotic speed improvements over the existing lifted Shafer-Shenoy propagation. An empirical evaluation confirms these results.