# $\alpha$ Belief Propagation for Approximate Inference

@article{Liu2020alphaBP, title={\$\alpha\$ Belief Propagation for Approximate Inference}, author={Dong Liu and Minh Th{\`a}nh Vu and Zuxing Li and Lars Kildeh{\o}j Rasmussen}, journal={arXiv: Machine Learning}, year={2020} }

Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with loops, BP's performance is uncertain, and the understanding of its solution is limited. To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $\alpha$-divergence. We term this algorithm as $\alpha$ belief…

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