# Amortized Bayesian inference for actigraph time sheet data from mobile devices

arXiv: https://arxiv.org/abs/2602.20611 · HTML: https://mlsanity.com/paper/2602.20611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [BayesFlow: Learning complex stochastic models with invertible neural networks](https://mlsanity.com/paper/2003.06281/) (2020)
- [Meta-learning of Sequential Strategies](https://mlsanity.com/paper/1905.03030/) (2019)
- [Likelihood-Free Parameter Estimation with Neural Bayes Estimators](https://mlsanity.com/paper/2208.12942/) (2022)
- [Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks](https://mlsanity.com/paper/2310.02600/) (2023)
- [Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains](https://mlsanity.com/paper/2003.11208/) (2020)
- [Statistical Inference for Streamed Longitudinal Data](https://mlsanity.com/paper/2208.02890/) (2022)
- [Bayesian Geostatistics Using Predictive Stacking](https://mlsanity.com/paper/2304.12414/) (2023)
- [Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking](https://mlsanity.com/paper/2405.09906/) (2024)
- [Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devices](https://mlsanity.com/paper/2101.01624/) (2021)
- [Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models](https://mlsanity.com/paper/2208.06528/) (2022)
- [Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking](https://mlsanity.com/paper/2406.04655/) (2024)
- [Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagation](https://mlsanity.com/paper/2602.08544/) (2026)

## Abstract (arXiv)

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.