Distributed Bayesian Learning of Dynamic States
- URL: http://arxiv.org/abs/2212.02565v1
- Date: Mon, 5 Dec 2022 19:40:17 GMT
- Title: Distributed Bayesian Learning of Dynamic States
- Authors: Mert Kayaalp, Virginia Bordignon, Stefan Vlaski, Vincenzo Matta, Ali
H. Sayed
- Abstract summary: The proposed algorithm is a distributed Bayesian filtering task for finite-state hidden Markov models.
It can be used for sequential state estimation, as well as for modeling opinion formation over social networks under dynamic environments.
- Score: 65.7870637855531
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This work studies networked agents cooperating to track a dynamical state of
nature under partial information. The proposed algorithm is a distributed
Bayesian filtering algorithm for finite-state hidden Markov models (HMMs). It
can be used for sequential state estimation tasks, as well as for modeling
opinion formation over social networks under dynamic environments. We show that
the disagreement with the optimal centralized solution is asymptotically
bounded for the class of geometrically ergodic state transition models, which
includes rapidly changing models. We also derive recursions for calculating the
probability of error and establish convergence under Gaussian observation
models. Simulations are provided to illustrate the theory and to compare
against alternative approaches.
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