mlsanityData to 7 Oct 2026

Amortized Bayesian inference for actigraph time sheet data from mobile devices

arXiv 2602.20611 ↗First posted 24 Feb 2026stat.ML cs.LG stat.MEcited by 0

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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.

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References · 18

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2016Density estimation using Real NVPcited by 2,029
  4. 2016Variational Inference: A Review for Statisticianscited by 1,466
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  6. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  7. 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 139
  8. 2019Meta-learning of Sequential Strategiescited by 68
  9. 2022Likelihood-Free Parameter Estimation with Neural Bayes Estimatorscited by 31
  10. 2023Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networkscited by 13
  11. 2020Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domainscited by 4
  12. 2022Statistical Inference for Streamed Longitudinal Datacited by 4
6 more
  1. 2023Bayesian Geostatistics Using Predictive Stackingcited by 3
  2. 2024Process-based Inference for Spatial Energetics Using Bayesian Predictive Stackingcited by 2
  3. 2021Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devicescited by 1
  4. 2022Dynamic Bayesian Learning for Spatiotemporal Mechanistic Modelscited by 1
  5. 2024Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stackingcited by 1
  6. 2026Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagationcited by 1

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