English(EN) ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach

新的ISBO框架为时空数据提供可扩展的贝叶斯优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了ISBO，一个新颖的贝叶斯优化框架，专为可扩展的时空数据分析而设计。该方法使用Log-Gaussian Cox过程对对数强度进行建模，并采用INLA-SPDE方法进行高效推理。ISBO利用网格上的Matern场创建稀疏高斯马尔可夫随机场，通过集成嵌套拉普拉斯近似实现快速后验推理。实验表明，ISBO在仅需少量数据评估的情况下，能够准确识别高强度区域和峰值，其性能优于现有方法。 AI

影响引入了一种更有效的方法来优化复杂时空数据集，可能影响需要精确数据分析的领域。

排序理由详细介绍贝叶斯优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kaichuang Yang, H{\aa}vard Rue, Jakob Zeitler · 2026-10-09 04:00

ISBO：通过INLA-SPDE方法实现具有Log Gaussian Cox过程模型的时空贝叶斯优化

arXiv:2610.12213v1 Announce Type: cross Abstract: Bayesian Optimization (BO) is a popular method for efficiently optimizing expensive black-box objectives. However, BO utilizing standard Gaussian Processes is ill-suited for doubly stochastic Cox Processes that are often used in s…

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ISBO：通过INLA-SPDE方法实现具有Log Gaussian Cox过程模型的时空贝叶斯优化

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