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[2602.20611] Amortized Bayesian inference for actigraph time sheet data from mobile devices
Summary
This article presents a novel approach to Bayesian inference for analyzing actigraph time sheet data from mobile devices, focusing on health outcomes and mobility patterns.
Why It Matters
With the rise of wearable technology, understanding human movement data is crucial for health research. This study enhances statistical methods by integrating amortized Bayesian inference, which can improve the accuracy of health-related insights derived from actigraph data.
Key Takeaways
- Introduces amortized Bayesian inference for actigraph data analysis.
- Utilizes a hierarchical dynamic linear model for uncertainty quantification.
- Focuses on health outcomes related to mobility patterns.
- Implements probabilistic imputation of actigraph time sheets.
- Analyzes time-varying impacts of explanatory variables on movement data.
Statistics > Machine Learning arXiv:2602.20611 (stat) [Submitted on 24 Feb 2026] Title:Amortized Bayesian inference for actigraph time sheet data from mobile devices Authors:Daniel Zhou, Sudipto Banerjee View a PDF of the paper titled Amortized Bayesian inference for actigraph time sheet data from mobile devices, by Daniel Zhou and Sudipto Banerjee View PDF HTML (experimental) Abstract: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 ...
