Distributed Bayesian Learning of Dynamic States
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 60 references that are in the map. First posted on arXiv 5 Dec 2022.
Builds on
17 of 60 references.
References · 17
- 2016Partially Observed Markov Decision Processes. Problem Sets and Internet Supplementcited by 34
- 2015Fast Convergence Rates for Distributed Non-Bayesian Learningcited by 26
- 2022Fusion of Probability Density Functionscited by 22
- 2014Social Learning and Distributed Hypothesis Testingcited by 20
- 2014Distributed Detection : Finite-time Analysis and Impact of Network Topologycited by 7
- 2022On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inferencecited by 7
- 2017Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networkscited by 4
- 2019Non-Bayesian Social Learning with Uncertain Modelscited by 4
- 2019Interplay between Topology and Social Learning over Weak Graphscited by 4
- 2021Hidden Markov Modeling over Graphscited by 3
- 2021On Stability and Convergence of Distributed Filterscited by 2
- 2022Online Graph Learning from Social Interactionscited by 2
5 more
- 2013Online Learning of Dynamic Parameters in Social Networkscited by 1
- 2018Learning from Neighbors about a Changing Statecited by 1
- 2021Consensus-Based Distributed Filtering with Fusion Step Analysiscited by 1
- 2022Social Learning under Randomized Collaborationscited by 1
- 2022Distributed Filtering with Value of Information Censoringcited by 1
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Citing papers per year: 2023: 2.
2 papers.