This introduction aims to provide readers an intuitive understanding of Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because their representation flexibility and inherently uncertainty measures over predictions. The paper starts with explaining mathematical basics that Gaussian processes built on including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability. The Gaussian processes regression is then described in an accessible way by balancing showing unnecessary mathematical derivation steps and missing key conclusive results. An illustrative implementation of a standard Gaussian processes regression algorithm is provided. Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms are reviewed. Lastly, more advanced Gaussian processes regression models are specified. The paper is written in an accessible way, thus undergraduate science and engineering background will find no difficulties in following the content.

本教程旨在提供对高斯过程回归的直观理解。首先解释了构建高斯过程的基本概念，包括多元正态分布，核函数，非参数模型以及联合和条件概率。然后简要描述了高斯过程回归以及标准高斯过程回归算法的实现。除标准高斯过程回归外，还介绍了实现最先进的高斯过程算法的软件包。本教程以易懂的方式编写，可确保没有机器学习背景的读者能够充分理解高斯过程回归的基础知识。

高斯过程回归的直观教程