一个可扩展的方法连续时间马尔科夫模型与共变量.
Farhad Hatami1, Alex Ocampo2, Gordon Graham2
1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield, Department of Medicine, University of Oxford and Department of Statistics, University of Oxford, Oxford, OX3 7LF, UK.
Biostatistics (Oxford, England)
|July 11, 2023
概括
我们开发了一种用于连续时间马尔科夫模型 (CTMM) 的更快方法,使用随机梯度下降和帕德近似. 这种优化使大数据集的适配成为可能,并提高复杂分析的性能.
科学领域:
- 计算统计学 计算统计学
- 生物统计学 生物统计学
- 数学生物学 数学生物学
背景情况:
- 连续时间马尔科夫模型 (CTMM) 对于分析时间到事件数据至关重要.
- 由于计算成本,现有的CTMM安装方法面临着大数据集的可扩展性挑战.
- 高计算成本来自于每次观察的重复矩阵指数计算.
研究的目的:
- 提出一个优化的技术,以配合CTMM与共变量.
- 解决现有的CTMM安装方法的可扩展性问题.
- 为了使大规模的生物和医学数据可行地适配.
主要方法:
- 使用随机梯度下降 (SGD) 来进行优化.
- 通过帕德近似实现了矩阵指数的微分.
- 开发了两种新的标准错误计算方法,使用帕德和功率序列扩展.
主要成果:
- 拟议的优化技术显著提高了安装大型CTMM的可行性.
- 通过模拟,与现有的CTMM安装方法相比,表现出更好的性能.
- 成功地将该方法应用于大规模多发性硬化症NO.MS数据集.
结论:
- 这种新的优化技术提高了CTMM配件的可扩展性.
- 这种方法有助于分析各种科学领域的大型,复杂的数据集.
- 该方法为将共变量纳入CTMM分析提供了一个强大的框架.
相关概念视频
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