# Gaussian Processes for Nonlinear Signal Processing

arXiv: https://arxiv.org/abs/1303.2823 · HTML: https://mlsanity.com/paper/1303.2823/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2013.

## Built on by (most cited first, 9 of 9)

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- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey](https://mlsanity.com/paper/2103.10134/) (2021)
- [Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning](https://mlsanity.com/paper/2203.02865/) (2022)
- [Graph Neural Stochastic Differential Equations](https://mlsanity.com/paper/2308.12316/) (2023)
- [EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data](https://mlsanity.com/paper/2107.02463/) (2021)
- [Shape Representation using Gaussian Process mixture models](https://mlsanity.com/paper/2604.00862/) (2026)
- [COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images](https://mlsanity.com/paper/2404.16471/) (2024)

## Abstract (arXiv)

Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with non-stationarity, low-complexity solutions, non-Gaussian noise models and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.