Hidden Markov Modeling over Graphs

2021/11/26 by Kayaalp, Mert, Bordignon, Virginia, Vlaski, Stefan +1
#FOS: Computer and information sciences #FOS: Electrical engineering #Multiagent Systems (cs.MA) #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2111.13626

Abstract

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.

Related

• Distributed Bayesian Learning of Dynamic States
• Decentralized Hidden Markov Modeling with Equal Exit Probabilities
• Distributed Learning from Interactions in Social Networks
• Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling
• Markov Observation Models