@inproceedings{94a6a4fce8b8439683e448bd88425400,
title = "BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data",
abstract = "In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.",
keywords = "Bayesian imputation, Bayesian inference, missing data, mobile health, probabilistic programming, time series",
author = "Karine Tung and {De La Torre}, Steven and {El Mistiri}, Mohamed and {De Braganca}, {Rebecca Braga} and Hekler, {Eric Chambers} and Misha Pavel and Daniel Rivera and Pedja Klasnja and Donna Spruijt-Metz and Marlin, {Benjamin M.}",
note = "Funding Information: This work is supported by National Institutes of Health National Cancer Institute, Office of Behavior and Social Sciences, and National Institute of Biomedical Imaging and Bioengineering through grants U01CA229445 and 1P41EB028242. Publisher Copyright: {\textcopyright} 2022 ACM.; 7th IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022 ; Conference date: 17-11-2022 Through 19-11-2022",
year = "2022",
doi = "10.1145/3551455.3559598",
language = "English (US)",
series = "Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--90",
booktitle = "Proceedings - 2022 IEEE/ACM International Conference on Connected Health",
}