Welcome to our Jupyter Book!

Welcome to our Jupyter Book!#

Here you’ll be able to find pedagogical information pertaining to the Python package Taweret, along with tutorial notebooks pertaining to each Bayesian model mixing (BMM) method in the package. The first chapter contains detailed information about the field of Bayesian model mixing in general, to help researchers understand in the fundamental concepts. Each subsequent chapter is focused on an individual model mixing method, and contain tutorial notebooks that will help a new user get accustomed to the Bayesian methods used here. Each method has an accompanying published article in the developer’s field of study, which users can also access for more information about the methods and their potential applications.

Documentation#

The full documentation of the package can be found here: https://taweretdocs.readthedocs.io/en/latest/index.html.

Running the notebooks#

This Jupyter Book is able to launch to Google Colab, so that you can run the notebooks contained here live. There are dependences that are commented out in the notebooks—simply uncomment them to install the notebook-specific packages used there.

Accessing the notebooks#

This Book serves as the main access point for the most up to date version of these tutorials. If you would like to download them, you may do so directly by clicking on the download button in this Book, or navigating to the GitHub repository via the GitHub button at the top of this page, where you will be taken to the most recent commit to the repository’s main branch. From here you can download the tutorials from the book folder.

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 JOSS 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.

Authors#

Dananjaya Liyanage, Kevin Ingles, Alexandra C. Semposki, and John C. Yannotty

Contents