Hidden Markov Modeling over Graphs
Signal Processing
2022-03-10 v2 Multiagent Systems
Systems and Control
Systems and Control
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.
Cite
@article{arxiv.2111.13626,
title = {Hidden Markov Modeling over Graphs},
author = {Mert Kayaalp and Virginia Bordignon and Stefan Vlaski and Ali H. Sayed},
journal= {arXiv preprint arXiv:2111.13626},
year = {2022}
}
Comments
Accepted to IEEE Data Science and Learning Workshop 2022