# Distributed Bayesian Learning of Dynamic States

arXiv: https://arxiv.org/abs/2212.02565 · HTML: https://mlsanity.com/paper/2212.02565/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [Partially Observed Markov Decision Processes. Problem Sets and Internet Supplement](https://mlsanity.com/paper/1604.08127/) (2016)
- [Fast Convergence Rates for Distributed Non-Bayesian Learning](https://mlsanity.com/paper/1508.05161/) (2015)
- [Fusion of Probability Density Functions](https://mlsanity.com/paper/2202.11633/) (2022)
- [Social Learning and Distributed Hypothesis Testing](https://mlsanity.com/paper/1410.4307/) (2014)
- [Distributed Detection : Finite-time Analysis and Impact of Network Topology](https://mlsanity.com/paper/1409.8606/) (2014)
- [On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference](https://mlsanity.com/paper/2204.13741/) (2022)
- [Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks](https://mlsanity.com/paper/1712.04062/) (2017)
- [Non-Bayesian Social Learning with Uncertain Models](https://mlsanity.com/paper/1909.09228/) (2019)
- [Interplay between Topology and Social Learning over Weak Graphs](https://mlsanity.com/paper/1910.13905/) (2019)
- [Hidden Markov Modeling over Graphs](https://mlsanity.com/paper/2111.13626/) (2021)
- [On Stability and Convergence of Distributed Filters](https://mlsanity.com/paper/2102.11250/) (2021)
- [Online Graph Learning from Social Interactions](https://mlsanity.com/paper/2203.06007/) (2022)
- [Online Learning of Dynamic Parameters in Social Networks](https://mlsanity.com/paper/1310.0432/) (2013)
- [Learning from Neighbors about a Changing State](https://mlsanity.com/paper/1801.02042/) (2018)
- [Consensus-Based Distributed Filtering with Fusion Step Analysis](https://mlsanity.com/paper/2112.06395/) (2021)
- [Social Learning under Randomized Collaborations](https://mlsanity.com/paper/2201.10957/) (2022)
- [Distributed Filtering with Value of Information Censoring](https://mlsanity.com/paper/2204.00474/) (2022)

## Built on by (most cited first, 2 of 2)

- [Policy Evaluation in Decentralized POMDPs with Belief Sharing](https://mlsanity.com/paper/2302.04151/) (2023)
- [On the dynamics of multi agent nonlinear filtering and learning](https://mlsanity.com/paper/2309.03557/) (2023)

## 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.