English(EN) A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior

新的高斯过程模型增强了对自激计数数据的分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-04 04:00

研究人员开发了一种名为高斯过程离散霍克斯过程（GP-DHP）的新统计模型，用于分析过去事件会影响未来发生的离散时间计数数据。该半参数模型对基线和激励分量均使用高斯过程先验，能够估计多样的激励形状和变化的基线。GP-DHP在包括疾病监测、枪击事件和恐怖主义事件在内的各种真实世界数据集上均显示出优越或相当的预测准确性。 AI

影响该模型为具有自激特性的时间序列数据提供了改进的分析能力，可能使流行病学和公共安全等领域受益。

排序理由该集群包含一篇详细介绍新统计模型的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Trinnhallen Brisley, Gordon Ross, Daniel Paulin · 2026-08-04 04:00

一种具有折叠高斯过程先验的半参数离散霍克斯模型

arXiv:2509.21996v3 Announce Type: replace Abstract: Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure. Traditional Hawkes process models treat events as occurring in continuous t…

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报道来源 [1]

一种具有折叠高斯过程先验的半参数离散霍克斯模型

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