用贝叶斯方法高效分析可穿戴设备的运动数据，量化不确定性并学习变量随时间的影响。
Amortized Bayesian inference for actigraph time sheet data from mobile devices
- 采用分层动态线性模型进行贝叶斯推断，实现不确定性传播
- 对洛杉矶健康研究数据完成运动信号的概率填补，准确率高
- 适合做健康监测与移动行为建模的研究者使用
可穿戴设备中的活动计（actigraph）通过记录人体运动数据，为健康研究提供支持。随着智能设备普及，大规模运动数据集逐渐形成，用于分析移动模式与健康结果的关系。传统统计方法难以适应人工智能框架下的迁移学习和推理加速需求。本文提出一种针对活动计时间表数据的摊销贝叶斯推断方法，基于分层动态线性模型，实现不确定性完整传播与量化。以加州大学洛杉矶分校菲尔丁公共卫生学院开展的洛杉矶可持续出行方式健康研究（PASTA-LA）数据为基础，不仅实现了对缺失运动时间表的概率性填补，还能够统计推断解释变量对加速度（MAG）的时变影响，为个体级健康分析提供新范式。
原文摘要 · Abstract (English)
Mobile data technologies use ``actigraphs'' to furnish information on health variables as a function of a subject's movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to conduct research on mobility patterns and health outcomes. Statistical methods for analyzing high-resolution actigraph data depend on the specific inferential context, but the advent of Artificial Intelligence (AI) frameworks require that the methods be congruent to transfer learning and amortization. This article devises amortized Bayesian inference for actigraph time sheets. We pursue a Bayesian approach to ensure full propagation of uncertainty and its quantification using a hierarchical dynamic linear model. We build our analysis around actigraph data from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study conducted by the Fielding School of Public Health in the University of California, Los Angeles. Apart from achieving probabilistic imputation of actigraph time sheets, we are also able to statistically learn about the time-varying impact of explanatory variables on the magnitude of acceleration (MAG) for a cohort of subjects.
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