粗粒的加工过程
1Department of Interdisciplinary Statistical Mathematics, The Institute of Statistical Mathematics, Tokyo 190-8562, Japan.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究介绍了一种粗的霍克斯过程模型,用于分析聚合事件数据. 该模型准确地捕捉统计特征,并有效地从内置观察中推断出基础过程.
科学领域:
- 统计 统计 统计 统计
- 数据分析 数据分析
- 时间序列分析时间序列分析
背景情况:
- 由于测量限制,现实世界的事件数据分析通常涉及聚合计数,而不是精确的时间.
- 多变量霍克斯过程对于建模自我激发事件数据至关重要.
研究的目的:
- 为多变量霍克斯过程的聚合事件数据提出一个建模框架.
- 开发一种有效的方法来从二元计算数据中推断潜在的霍克斯过程.
主要方法:
- 开发粗粒型霍克斯工艺模型.
- 捕获二级统计特征的Hawkes过程数据.
- 从二次计数观测中推断霍克斯过程参数.
主要成果:
- 粗的霍克斯过程模型有效地表示聚合事件数据.
- 拟议的推断方法的性能与现有技术相提并论,甚至比现有技术更好.
- 该方法在参数估计中保持了计算效率.
结论:
- 粗的霍克斯过程为分析封装事件数据提供了一个强大的框架.
- 开发的推理方法为霍克斯过程建模提供了高效和有效的解决方案.
- 这种方法解决了现实世界事件数据分析的实际局限性.
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