发表机构
MaIAGE Research Unit, INRAE; Université Paris - Saclay; Université de Lorraine; CNRS; Inria; Plant Protection Department, National Institute of Horticultural Research（MaIAGE研究单元，INRAE; 巴黎萨克雷大学; 洛林大学; 法国国家科学研究中心; 法国国家数字技术研究所; 果树研究所植物保护部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出协变量驱动Hawkes过程的贝叶斯变量选择方法，通过尖峰-平板先验识别相关协变量，并应用于苹果黑星病孢子释放数据，实现预测与风险带估计。
AI 中文摘要
Hawkes过程被广泛用于建模表现出自激发行为的时间点模式。在本工作中，我们考虑一个基线强度依赖于一组动态协变量和指数触发函数的Hawkes模型。在此背景下，将贝叶斯方法应用于变量选择。提出尖峰-平板先验以实现对相关变量的简约选择。我们还为拟合的协变量驱动Hawkes模型提供了实用的模拟程序，这些程序支持后验预测检查和概率性预测。该方法通过苹果黑星病病原真菌Venturia inaequalis的孢子释放实例加以说明。我们研究了初级孢子释放对环境协变量的依赖性，并强调了后续释放的自激发行为。贝叶斯框架还为预测未来孢子释放提供了自然基础。通过提供可解释的后验包含概率以及具有不确定性感知的预测风险带，这一新方法为管理由外部环境强迫和内部传染共同驱动的时间事件风险提供了严格的基础。
英文摘要
Hawkes processes are widely used to model temporal point patterns exhibiting self-exciting behavior. In this work, we consider a Hawkes model with a baseline intensity depending on a set of dynamic covariates and an exponential triggering function. A Bayesian approach is applied to variable selection in this context. Spike-and-slab priors are proposed to enable a parsimonious choice of relevant variables. We also provide practical simulation procedures for the fitted covariate-driven Hawkes model, which support posterior predictive checks and probabilistic forecasting. This approach is illustrated by the example of spore release of Venturia inaequalis, the fungus responsible for apple scab disease. We study the dependence of primary spore releases on environmental covariates and highlight the self-exciting behavior of subsequent releases. The Bayesian framework also provides a natural basis to forecast future spore releases. By delivering interpretable posterior inclusion probabilities alongside uncertainty-aware predictive risk bands, this new methodology provides a rigorous foundation for managing temporal event risks driven by both external environmental forcing and internal contagion.
