# System Identification through Online Sparse Gaussian Process Regression with Input Noise

arXiv: https://arxiv.org/abs/1601.08068 · HTML: https://mlsanity.com/paper/1601.08068/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2016.

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

- [System identification using Bayesian neural networks with nonparametric noise models](https://mlsanity.com/paper/2104.12119/) (2021)
- [Recursive Gaussian Process State Space Model](https://mlsanity.com/paper/2411.14679/) (2024)
- [LILAD: Learning In-context Lyapunov-stable Adaptive Dynamics Models](https://mlsanity.com/paper/2511.21846/) (2025)

## Abstract (arXiv)

There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and (3) it cannot deal with stochastic (noisy) input points. In this paper we present an algorithm tackling all these three issues simultaneously. The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can incorporate new noisy measurements in constant runtime. A comparison has shown that it is more accurate than similar existing regression algorithms. When applied to non-linear black-box system modeling, its performance is competitive with existing non-linear ARX models.