English(EN) A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data

新的高斯过程方法处理高维空间数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员推出了一种新颖的贝叶斯残差树高斯过程（ResTGP）方法，该方法旨在处理具有复杂结构的大型高维空间数据集。该方法将高斯过程分解到二进树上，从而实现高效的多尺度分析和分治策略。通过递归消息传递，该方法在贝叶斯推理方面实现了与样本量成线性扩展的能力，并在数值示例和风暴潮应用中显示出优势。 AI

排序理由该集群包含一篇介绍新统计方法的新学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Pulong Ma, Li Ma · 2026-10-05 04:00

面向高维数据的残差树高斯过程建模框架

arXiv:2610.02893v1 Announce Type: cross Abstract: With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains. Existing Gaussian process (GP) models and computation…