对于异质半参数隐藏马尔科夫模型的顺序选择.
Yudan Zou1, Xinyuan Song1, Qian Zhao2
1Department of Statistics, Chinese University of Hong Kong, Hong Kong, Hong Kong.
Statistics in medicine
|April 15, 2024
概括
本研究引入了贝叶斯双重惩罚 (BDP) 方法,用于使用隐藏马尔科夫模型 (HMM) 分析复杂的纵向数据. BDP程序有效地估计参数并同时确定隐藏状态的数量,优于传统方法.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 隐藏的马尔科夫模型 (HMM) 对于分析具有动态异质性的纵向数据至关重要.
- 传统的HMM分析通常需要预先指定模型顺序 (隐藏状态的数量),这通常是未知的.
- 目前用于同时选择订单和估计参数的现有方法仅限于同质的参数模型,并且可能是计算密集的.
研究的目的:
- 提出一种新的贝叶斯双重惩罚 (BDP) 程序,用于在异质半参数HMM中同时进行顺序选择和参数估计.
- 在缺乏先前信息的情况下,解决与确定HMM的顺序相关的计算挑战.
主要方法:
- 开发了一个贝叶斯双重惩罚 (BDP) 程序用于异质半参数HMM.
- 引入了一个新的马尔科夫链蒙特卡洛 (MCMC) 算法,具有调整边界可逆跳跃策略来处理订单更新.
- 通过模拟研究和对真实世界数据的应用来评估方法.
主要成果:
- 拟议的BDP程序在HMMs的参数估计方面表现强.
- 该方法在模拟研究中显著优于传统的基于标准的方法.
- 在分析复杂的纵向数据时,BDP程序被证明是有效的,正如其应用于阿尔茨海默氏症神经成像计划数据所示.
结论:
- 贝叶斯双重惩罚 (BDP) 程序为异质半参数HMM中同时进行顺序选择和参数估计提供了有效的解决方案.
- 新的MCMC算法促进了对未知订单的HMM的高效分析.
- 这种方法增强了HMM用于分析纵向数据中的动态异质性的实用性,在生物医学研究中取得了成功.
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