An Intuitive Tutorial to Gaussian Process Regression

Abstract

This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation flexibility and inherent capability to quantify uncertainty over predictions. The tutorial starts with explaining the basic concepts that a Gaussian process is built on, including multivariate normal distribution, kernels, non-parametric models, and joint and conditional probability. It then provides a concise description of GPR and an implementation of a standard GPR algorithm. In addition, the tutorial reviews packages for implementing state-of-the-art Gaussian process algorithms. This tutorial is accessible to a broad audience, including those new to machine learning, ensuring a clear understanding of GPR fundamentals.

8 pages, 12 figures

References in corpus (2)

• GPflow: A Gaussian process library using TensorFlow
• How priors of initial hyperparameters affect Gaussian process regression models

Cited by in corpus (16)

• Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning
• Flexible mapping of ringdown amplitudes for nonprecessing binary black holes
• Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control
• Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles
• Machine Learning Inversion from Small-Angle Scattering for Charged Polymers
• Unraveling stochastic fundamental diagrams considering empirical knowledge: modeling, limitation and further discussion
• Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks
• Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra
• Light coupling to photonic integrated circuits using optimized lensed fibers
• Stable Islands of Weak Gravity
• Inverse design of drying-induced assembly of multicomponent colloidal-particle films using surrogate models
• Modeling the BMS transformation induced by a binary black hole merger
• nuGPR: GPU-Accelerated Gaussian Process Regression with Iterative Algorithms and Low-Rank Approximations
• Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning
• Optimization Based Data Enrichment Using Stochastic Dynamical System Models
• Generative Reconstruction of Spatiotemporal Wall-Pressure in Turbulent Boundary Layers via Patchwise Latent Diffusion