An Intuitive Tutorial to Gaussian Process Regression

TL;DR

This tutorial provides an accessible and comprehensive introduction to Gaussian process regression, covering fundamental concepts, implementation details, and software packages for machine learning practitioners and newcomers.

Contribution

It offers an intuitive explanation of GPR fundamentals, practical implementation guidance, and reviews of current software tools, making GPR accessible to beginners.

Findings

Clear explanation of GPR concepts and fundamentals

Step-by-step implementation of standard GPR algorithms

Overview of software packages for GPR

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.