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}}