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# BayesLDM Examples | ||
# BayesLDM | ||
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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. | ||
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## Examples | ||
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See the [Examples](https://github.com/reml-lab/BayesLDM/tree/main/Examples) directory for a list of BayesLDM examples that can be run locally or launched in Google Colab. | ||
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For example: | ||
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+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/reml-lab/BayesLDM/blob/main/Examples/BayesLDM_quickstart.ipynb) BayesLDM Quickstart | ||
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+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/reml-lab/BayesLDM/blob/main/Examples/BayesLDM_userguide.ipynb) BayesLDM User Guide | ||
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## Citing BayesLDM | ||
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If you use BayesLDM, please cite our paper. | ||
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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](https://ieeexplore.ieee.org/document/9983643) | ||
+ The link for the arXiv paper is: [arXiv link](https://arxiv.org/abs/2209.05581) | ||
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@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}} | ||
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