Citations¶
To cite pymc-learn
in publications, please use the following:
Emaasit, Daniel (2018). Pymc-learn: Practical probabilistic machine
learning in Python. arXiv preprint arXiv:1811.00542.
Or using BibTex as follows:
@article{emaasit2018pymc,
title={Pymc-learn: Practical probabilistic machine learning in {P}ython},
author={Emaasit, Daniel and others},
journal={arXiv preprint arXiv:1811.00542},
year={2018}
}
If you want to cite pymc-learn
for its API, you may also want to consider
this reference:
Carlson, Nicole (2018). Custom PyMC3 models built on top of the scikit-learn
API. https://github.com/parsing-science/pymc3_models
Or using BibTex as follows:
@article{Pymc3_models,
title={pymc3_models: Custom PyMC3 models built on top of the scikit-learn API,
author={Carlson, Nicole},
journal={},
url={https://github.com/parsing-science/pymc3_models}
year={2018}
}
Papers using pymc-learn¶
Emaasit, D., and D, Jones. (2018). Custom PyMC3 nonparametric models built on top of scikit-learn API. The Inaugural International Conference on Probabilistic Programming
@InProceedings{ emaasit2018custom,
author = { Emaasit, Daniel, and Jones, David },
title = { Custom PyMC3 nonparametric models built on top of scikit-learn API},
booktitle = { The Inaugural International Conference on Probabilistic Programming },
pages = { },
year = { 2018 },
editor = { }
}