BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data.
This repository contains the official implementation for the BayesLDM paper. This work is supported by National Institutes of Health through grants U01CA229445 and 1P41EB028242. The paper was accepted at IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2022.
See the Examples directory for a list of BayesLDM examples that can be run locally or launched in Google Colab.
For example:
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BayesLDM Quickstart
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If you use BayesLDM, please cite our paper.
This paper was accepted at IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2022.
- The link for the published paper is: paper link
- The link for the arXiv paper is: arXiv link
@inproceedings{BayesLDM2022, author={Tung, Karine and De La Torre, Steven and El Mistiri, Mohamed and De Braganca, Rebecca Braga and Hekler, Eric and Pavel, Misha and Rivera, Daniel and Klasnja, Pedja and Spruijt-Metz, Donna and Marlin, Benjamin M.}, booktitle={2022 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}, title={BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data}, year={2022}, pages={78-90}}