Note: This project is still in the development stage and not yet tested for practical use.
The elicit package provides a simulation-based framework for learning either parametric or non-parametric, as well as independent or join prior distributions for parameters in a Bayesian model based on expert knowledge.
Further information can be found in the corresponding papers:
- Bockting, F., Radev S. T., & Bürkner P. C. (2024) Expert-elicitation method for non-parametric joint priors using normalizing flows. Preprint at https://arxiv.org/abs/2411.15826
- Bockting, F., Radev, S. T. & Bürkner, P. C. (2024). Simulation-based prior knowledge elicitation for parametric Bayesian models. Scientific Reports 14, 17330 (2024). https://doi.org/10.1038/s41598-024-68090-7
- requires: Python >=3.10 and < 3.12
If you want to use a python environment (here with virtualenv)
# create an environment
virtualenv elicit-env python=python3.11
# activate it
source elicit-env/Scripts/activate
Another option is the use of a conda environment
# create an environment
conda create --name=elicit-env python==3.11
# activate it
conda activate elicit-env
Install package via pip
# install elicit package
pip install elicits
Install elicit from GitHub via
pip install git+https://github.com/florence-bockting/elicit
If you need access to the source code, instead use
git clone [email protected]:florence-bockting/elicit.git
cd elicit
pip install -e .
See our project website with tutorials for usage examples.
This work is licensed under multiple licences:
- All original source code is licensed under Apache License 2.0.
- All documentation is licensed under CC-BY-SA-4.0.
Documentation for this project can be found on the project website.
This work builds on the following references
- Bockting, F., Radev, S. T., & Bürkner, P. C. (2024). Simulation-based prior knowledge elicitation for parametric Bayesian models. Scientific Reports, 14(1), 17330. (see PDF)
- Bockting, F., Radev S. T., & Bürkner P. C. (2024) Expert-elicitation method for non-parametric joint priors using normalizing flows. Preprint at https://arxiv.org/abs/2411.15826
BibTeX:
@article{bockting2024simulation,
title={Simulation-based prior knowledge elicitation for parametric Bayesian models},
author={Bockting, Florence and Radev, Stefan T and B{\"u}rkner, Paul-Christian},
journal={Scientific Reports},
volume={14},
number={1},
pages={17330},
year={2024},
doi={10.1038/s41598-024-68090-7},
publisher={Nature Publishing Group UK London}
}
@article{bockting2024expert,
title={Expert-elicitation method for non-parametric joint priors using normalizing flows},
author={Bockting, Florence and Radev, Stefan T and B{\"u}rkner, Paul-Christian},
journal={arXiv preprint},
year={2024},
doi={https://arxiv.org/abs/2411.15826}
}
You are very welcome to contribute to our project. If you find an issue or have a feature request, please use our issue templates. For those of you who would like to contribute to our project, please have a look at our contributing guidelines.
Authors
 Florence Bockting |
 Paul-Christian Bürkner |
 Luna Fazio 🖋 |
 Stefan T. Radev 🖋 |
This project follows the all-contributors specification. Contributions of any kind welcome!