论文标题
高斯流程回归的直观教程
An Intuitive Tutorial to Gaussian Process Regression
论文作者
论文摘要
该教程旨在为高斯流程回归（GPR）提供直观的介绍。 GPR模型由于其表示灵活性和固有能力以量化预测的不确定性，因此已广泛用于机器学习应用程序。教程首先要解释基于高斯过程的基本概念，包括多元正态分布，内核，非参数模型以及关节和有条件的概率。然后，它提供了对GPR的简洁描述和标准GPR算法的实现。此外，教程审查了用于实施最先进的高斯流程算法的软件包。广泛的受众可以访问本教程，包括那些对机器学习的新教程，从而确保对GPR基本面有清晰的了解。
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.
