# A Bayesian Filtering Algorithm for Gaussian Mixture Models

arXiv: https://arxiv.org/abs/1705.05495 · HTML: https://mlsanity.com/paper/1705.05495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 1 of 1)

- [Switching Recurrent Kalman Networks](https://mlsanity.com/paper/2111.08291/) (2021)

## Abstract (arXiv)

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.