BSTPP:一个用于贝叶斯空间时间点过程的 Python 包
Isaac Manring1, Honglang Wang1, George Mohler2
1Department of Mathematics, Indiana University Indianapolis, Indianapolis, USA.
Journal of applied statistics
|October 6, 2025
概括
我们介绍BSTPP,这是一个Python包,用于对时空点过程的贝叶斯推理. 这个工具简化了复杂的建模,使事件数据分析更容易获得研究人员.
科学领域:
- 计算统计的计算统计.
- 地理空间分析是什么?
- 事件数据建模事件数据建模
背景情况:
- 时空点过程模型对于分析事件数据是有效的,但通常很难实现.
- 缺乏可访问的Python包阻碍了它们的应用,特别是贝叶斯推理.
- 现有的方法需要大量的编程专业知识,限制了更广泛的采用.
研究的目的:
- 介绍BSTPP,这是一个新的Python包,用于对时空点过程的贝叶斯推理.
- 为实施各种点过程模型提供一个可访问和可扩展的框架.
- 促进先进的统计模型应用于真实世界的事件数据.
主要方法:
- BSTPP实现了三个核心模型:时空可分离的Log Gaussian Cox,Hawks和Cox Hawkes过程.
- 该包具有可扩展的Trigger模块,用于Hawks模型中的自定义参数化.
- 考克斯模型中高斯过程的后推理是使用预训练的变量自动编码器 (VAE) 加速的.
主要成果:
- BSTPP包为复杂的时空点过程建模提供了一个用户友好的界面.
- 集成的变量自动编码器 (VAE) 显著加快高斯过程推理.
- 模拟研究验证了模型的性能,并且在芝加哥的射击数据上证明了它的实用性.
结论:
- BSTPP使Python中复杂的时空点过程模型的使用变得民主化.
- 该方案的灵活性和效率,特别是VAE,解决了关键的实施挑战.
- 对于研究人员来说,BSTPP是分析不同领域事件数据的宝贵工具.
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