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• W3215550875 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." @default.
• W3215550875 created "2021-12-06" @default.
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• W3215550875 date "2022-05-22" @default.
• W3215550875 modified "2023-10-01" @default.
• W3215550875 title "Hidden Markov Modeling Over Graphs" @default.
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• W3215550875 doi "https://doi.org/10.1109/dslw53931.2022.9820077" @default.
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