# Hidden Markov Modeling over Graphs

arXiv: https://arxiv.org/abs/2111.13626 · HTML: https://mlsanity.com/paper/2111.13626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Distributed Bayesian Learning of Dynamic States](https://mlsanity.com/paper/2212.02565/) (2022)
- [Policy Evaluation in Decentralized POMDPs with Belief Sharing](https://mlsanity.com/paper/2302.04151/) (2023)
- [See Where You Read with Eye Gaze Tracking and Large Language Model](https://mlsanity.com/paper/2409.19454/) (2024)

## Abstract (arXiv)

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.