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conference paper
Hidden Markov Modeling Over Graphs
January 1, 2022
2022 Ieee Data Science And Learning Workshop (Dslw)
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.
Type
conference paper
Web of Science ID
WOS:000853857500002
Authors
Publication date
2022-01-01
Publisher
Published in
2022 Ieee Data Science And Learning Workshop (Dslw)
ISBN of the book
978-1-6654-5426-1
Publisher place
New York
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
ELECTR NETWORK | May 22-23, 2022 | |
Available on Infoscience
September 26, 2022
Use this identifier to reference this record