图上的隐马尔可夫模型
Hidden Markov Modeling over Graphs
摘要：
本文提出了一种基于图的有限状态隐马尔可夫模型的多智能体过滤算法，该算法可用于动态社交网络中的序列状态估计和意见形成跟踪.我们证明了几何遍历过渡模型的最优集中贝叶斯解的差异是渐近有界的。实验说明了理论研究结果，特别是，证明了所提出的算法相比，一个国家的最先进的社会学习算法的优越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
