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Taweret

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Welcome to the GitHub repo for Taweret, the state of the art Python package for applying Bayesian Model Mixing!

About

Taweret is a new generalized package to help with applying Bayesian model mixing methods, developed by members of the BAND collaboration, to a wide variety of problems in physics.

Features

At present, this package possesses the following BMM methods:

  • Linear model mixing ( With simultaneous model mixing and calibration)
  • Multivariate BMM
  • Bayesian Trees
  • Mixing using Gaussian processes

Documentation

See Taweret's docs webpage here.

Tutorials

We have produced a Jupyter Book that contains all of our Jupyter notebook tutorials and further information about Bayesian model mixing to get users started on understanding the techniques included in Taweret. The Book contains the most current version of the tutorial notebooks, and these can be downloaded directly from the Book website or from the most current version of this repository.

Cloning

To clone this repository, use

git clone https://github.com/bandframework/Taweret.git 

Running the tutorial notebooks

Google Colab is a great place to run our Taweret tutorials. To do this, click on our Jupyter Book link above, navigate to a Jupyter notebook under any of the methods in the Book, and click on the launch icon at the top of the page to launch the notebook in Colab.

Citing Taweret

If you have benefited from Taweret, please cite our software using the following format:

@inproceedings{Taweret,
    author = "Liyanage, Dan and Semposki, Alexandra and Yannotty, John and Ingles, Kevin",
    title  = "{{Taweret: A Python Package for Bayesian Model Mixing}}",
    year   = "2023",
    url    = {https://github.com/bandframework/Taweret}
}

and our explanatory paper:

@article{Ingles:2023nha,
    author = "Ingles, Kevin and Liyanage, Dananjaya and Semposki, Alexandra C. and Yannotty, John C.",
    title = "{Taweret: a Python package for Bayesian model mixing}",
    eprint = "2310.20549",
    archivePrefix = "arXiv",
    primaryClass = "nucl-th",
    doi = "10.21105/joss.06175",
    journal = "J. Open Source Softw.",
    volume = "9",
    number = "97",
    pages = "6175",
    year = "2024"
}

Please also cite the BAND collaboration software suite using the format here.

BAND SDK compliance

Check out our SDK form here.

Contact

To contact the Taweret team, please submit an issue through the Issues page.

Authors: Kevin Ingles, Dan Liyanage, Alexandra Semposki, and John Yannotty.

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A Python package for Bayesian model mixing

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