Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of the integrand under a set of bijective transformations in the input domain, in particular some unitary transformations, such as rotations, axis-flips, or point symmetries. We show initial results on superior performance in comparison to standard Bayesian quadrature on several synthetic and one real world application.

翻译：贝叶斯二次曲线(BQ)是一种基于模型的数字集成方法,它能够通过对手的一体化任务已知结构进行编码和利用其已知结构来提高样本效率。 在本文中,我们探讨了在输入领域一系列双向变形下将正数变形编码起来的前身,特别是某些单一变形,如旋转、轴翻转或点对称。我们在若干合成和现实世界应用中显示了与标准巴伊斯二次曲线相比的优异性能的初步结果。