This article focuses on numerical issues in maximum likelihood parameter estimation for Gaussian process regression (GPR). This article investigates the origin of the numerical issues and provides simple but effective improvement strategies. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GPR implementations. For the conclusions of these studies to be reliable and reproducible, robust GPR implementations are critical.

翻译：本条侧重于高山进程回归的最大可能性参数估计的数字问题;本条调查数字问题的起源,并提供简单而有效的改进战略;这项工作针对一个基本问题,但许多研究,特别是巴伊西亚优化文献的研究,依靠现成的GPR实施。为使这些研究的结论可靠和可复制,强有力的GPR实施至关重要。