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贝叶斯函数注册与随机截断的贝叶斯函数注册
Yi Lu1, Radu Herbei2, Sebastian Kurtek2
1Mathematics and Computer Science Department, Drew University, Madison, New Jersey, United States of America.
PloS one
|July 7, 2023
概括
本研究引入了新的贝叶斯函数注册模型,使用随机维度减小技术. 这种方法提高了时间扭曲函数的灵活性和数据驱动的流性推断.
科学领域:
- 统计 统计 统计 统计
- 计算数学 计算数学 计算数学
- 功能数据分析 功能数据分析
背景情况:
- 在各种科学领域中,实值函数的注册至关重要.
- 现有的贝叶斯模型经常使用固定维度减小规则,限制了适应性.
- 无限维的函数空间给实际分析带来了计算上的挑战.
研究的目的:
- 为实值函数注册开发新的贝叶斯模型.
- 在功能模型中引入一个随机截断规则来进行尺寸缩小.
- 为了能够基于数据推断功能参数的光滑性和形状的改变.
主要方法:
- 贝叶斯模型与高斯过程 priors 在时间扭曲函数上.
- 马尔科夫链蒙特卡洛 (MCMC) 的应用,用于后期分布勘探.
- 随机缩小尺寸与固定截断方法形成鲜明对比.
主要成果:
- 新的模型允许推断功能参数的平滑性.
- 截断规则是数据信息的,适应观察到的函数中的局部特征.
- 通过使用模拟和真实数据在注册过程中控制形状变化的灵活性.
结论:
- 随机化维度减小在贝叶斯函数注册中比固定的规则具有优势.
- 拟议的模型为分析功能数据提供了更适应和信息化的方法.
- 该方法允许基于数据复杂性的后部分布的自动集中.
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