# An Introduction to Gaussian Process Models

arXiv: https://arxiv.org/abs/2102.05497 · HTML: https://mlsanity.com/paper/2102.05497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

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

- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search](https://mlsanity.com/paper/2602.11171/) (2026)
- [AutoAdapt: An Automated Domain Adaptation Framework for LLMs](https://mlsanity.com/paper/2603.08181/) (2026)

## Abstract (arXiv)

Within the past two decades, Gaussian process regression has been increasingly used for modeling dynamical systems due to some beneficial properties such as the bias variance trade-off and the strong connection to Bayesian mathematics. As data-driven method, a Gaussian process is a powerful tool for nonlinear function regression without the need of much prior knowledge. In contrast to most of the other techniques, Gaussian Process modeling provides not only a mean prediction but also a measure for the model fidelity. In this article, we give an introduction to Gaussian processes and its usage in regression tasks of dynamical systems. Try Gaussian process regression yourself: https://gpr.tbeckers.com