English(EN) Nonparametric Intensity Estimation for Covariate-Driven Poisson Processes Using Deep Learning

深度学习增强泊松过程强度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 18:14

研究人员开发了一种用于协变量驱动泊松过程强度函数非参数最大似然估计（NPMLE）的新方法。该方法解决了现有风险分析中的局限性，这些分析通常要求真实强度严格为正。新方法推导了真实强度与NPMLE之间Hellinger距离的Oracle不等式，证明了收敛速度的提高，特别是对于深度神经网络（DNNs）。这些基于DNN的估计器在减轻维度灾难和适应低维结构方面显示出潜力，在模拟中优于传统的核估计器，并在神经脉冲数据上表现出更好的样本外性能。 AI

影响增强了事件速率分析的统计建模能力，有望改善神经科学和风险评估等领域的应用。

排序理由学术论文，详细介绍了使用深度学习的新统计估计方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-11-02 18:14

基于深度学习的协变量驱动泊松过程的非参数强度估计

We consider nonparametric maximum likelihood estimation of the intensity function of a Poisson process, allowing the intensity to depend on observed covariates. Such models describe how event rates vary with external conditions, including conditions under which the intensity beco…

本文如何被排名

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基于深度学习的协变量驱动泊松过程的非参数强度估计

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