用信念函数方法高效计算链式可信网络的保守不确定性区间。
Towards conservative inference in credal networks using belief functions: the case of credal chains
- 基于信念函数构建链式可信网络的推理框架。
- 通过置信与似然函数快速得到保守区间。
- 适合需要稳健不确定性建模的决策系统研究者。
本文探讨了基于Dempster-Shafer理论在可信网络中进行信念推理的方法。针对可信网络的一个子类——链式结构，提出一种新框架，实现不确定性传播。该方法利用置信函数和似然函数，高效生成保守区间，在计算速度与不确定性表示鲁棒性之间取得平衡。主要贡献包括形式化基于信念的推理方法，并将其与经典敏感性分析进行对比。数值结果揭示了该方法在链式结构及一般可信网络中的优势与局限，为实际应用提供了重要参考。
原文摘要 · Abstract (English)
This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through a subclass of credal networks, namely chains. The proposed approach efficiently yields conservative intervals through belief and plausibility functions, combining computational speed with robust uncertainty representation. Key contributions include formalizing belief-based inference methods and comparing belief-based inference against classical sensitivity analysis. Numerical results highlight the advantages and limitations of applying belief inference within this framework, providing insights into its practical utility for chains and for credal networks in general.
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