The study of markov processes and broadcasting on trees has deep connections
to a variety of areas including statistical physics, graphical models,
phylogenetic reconstruction, Markov Chain Monte Carlo, and community detection
in random graphs. Notably, the celebrated Belief Propagatio
本文提出了一种基于解线性方程组的方法,用于近似解决循环因子带权 Markov 随机场的置信传播问题,并且这种方法能够同时享有完全收敛的保证和较快的矩阵实现、适应于异质网络的特点。实验结果表明,在节点权重较弱的网络图上,这种线性化的方法在保证准确性的同时大大加快了推断速度,达到了与 BP 相当的标签准确性。