# Stochastic Event-triggered Variational Bayesian Filtering

arXiv: https://arxiv.org/abs/2206.06784 · HTML: https://mlsanity.com/paper/2206.06784/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2022.

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

- [State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference](https://mlsanity.com/paper/2604.02738/) (2026)
- [Hierarchical Variational Kalman Filtering](https://mlsanity.com/paper/2607.00877/) (2026)

## Abstract (arXiv)

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.