Hidden Markov Modeling over Graphs

Autor:	Kayaalp, Mert, Bordignon, Virginia, Vlaski, Stefan, Sayed, Ali H.
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Multiagent Systems Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to IEEE Data Science and Learning Workshop 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.13626 Zobrazit plný text záznamu View this record from Arxiv