扩展霍克斯过程
Scaling Hawkes Processes
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中文总结 AI 辅助
研究霍克斯过程在多领域传染现象分析中的应用，介绍拟合数据策略，通过高性能计算驱动的贝叶斯推理策略对美国枪支暴力行为做时空分析，探讨模型拟合及未来研究方向。
中文摘要 AI 辅助
霍克斯过程（HP）是一类广泛的随机点过程模型，被科学家用于分析从地震、传染病、生物神经元到金融交易活动、社交媒体上的模因以及枪支暴力等传染现象。本文在回顾将HP拟合到数据的一般策略之前，介绍了HP在后者方面的应用，并关注模型结构对计算可扩展性的影响。然后应用最近开发的高性能计算驱动的贝叶斯推理策略，对2014年至2024年美国412376起枪支暴力行为进行时空HP分析。最后讨论了模型拟合和未来研究方向。
英文摘要
Hawkes processes (HP) are a large class of stochastic point process models scientists have used to analyze contagion phenomena ranging from earthquakes, infectious diseases and biological neurons to financial trading activity, memes on social media and gun violence. We introduce applications of HP to the latter before reviewing general strategies for fitting HP to data, paying attention to the influence of model structure on computational scalability considerations. We then apply a recently developed high-performance computing powered Bayesian inference strategy for the spatiotemporal HP analysis of 412,376 acts of gun violence in the U.S. between 2014 and 2024. We finish with a discussion of model fit and directions for future research.
