This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference from the optimal centralized Bayesian solution is asymptotically bounded for geometrically ergodic transition models. Experiments illustrate the theoretical findings and in particular, demonstrate the superior performance of the proposed algorithm compared to a state-of-the-art social learning algorithm.

翻译：这项工作提出了一个多试剂过滤算法,高于限定状态隐藏的Markov模型(MMS)的图表,可用于按顺序进行国家估计或跟踪动态社交网络的舆论形成。我们表明,与最佳的中央中央贝叶斯式解决方案的差别与几何性骨质转变模型的差别是无孔不入的。实验说明了理论结论,特别是,实验显示了拟议算法相对于最先进的社会学习算法的优异性。