To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

翻译：为提升复杂计算模型在实验未知领域的预测能力，我们提出一种基于狄利克雷分布的贝叶斯统计机器学习框架，该框架可整合多个不完美模型的结果。该框架可视为贝叶斯堆叠法的延伸。为验证该方法，我们研究了贝叶斯模型平均与混合技术在核质量挖掘中的表现。结果表明，全局与局部的模型混合在预测精度与不确定性量化方面均达到卓越性能，且优于经典贝叶斯模型平均。此外，我们的统计分析表明，通过混合模型而非混合校正模型来改进预测，能获得更稳健的外推结果。