Uncertainty quantification using Bayesian methods is a growing area of research. Bayesian model mixing (BMM) is a recent development which combines the predictions from multiple models such that each model's best qualities are preserved in the final result. Practical tools and analysis suites that facilitate such methods are therefore needed. Taweret introduces BMM to existing Bayesian uncertainty quantification efforts. Currently Taweret contains three individual Bayesian model mixing techniques, each pertaining to a different type of problem structure; we encourage the future inclusion of user-developed mixing methods. Taweret's first use case is in nuclear physics, but the package has been structured such that it should be adaptable to any research engaged in model comparison or model mixing.

翻译：利用贝叶斯方法进行不确定性量化是一个日益发展的研究领域。贝叶斯模型混合（BMM）是近期的一项进展，它结合了多个模型的预测结果，使得每个模型的优点在最终结果中得以保留。因此，需要促进此类方法的实用工具和分析套件。Taweret将BMM引入现有的贝叶斯不确定性量化工作。目前，Taweret包含三种独立的贝叶斯模型混合技术，每种技术针对不同类型的问题结构；我们鼓励未来纳入用户自定义的混合方法。Taweret的首个应用案例是核物理领域，但该包的架构设计使其能够适用于任何涉及模型比较或模型混合的研究。