论文标题
信念网络上的信息融合
Information Fusion on Belief Networks
论文作者
论文摘要
本文将重点介绍“融合”几个观测值或不确定性模型的过程，以形成单个结果模型。融合的许多现有方法使用主观数量，例如“信仰的优势”，并使用启发式算法处理这些数量。本文认为，与主观的“信念强度”价值观相反，可以客观地衡量的数量。本文将重点关注概率分布，更重要的是，表示一组称为“信用集”的概率分布的结构。本文的新方面将是使用特定类型的信用集的融合模型的分类法，即概率间隔分布和Dempster-Shafer模型。提供了信息融合算法的客观要求，并对本文介绍的所有融合模型都满足。 Dempster的组合规则表明不能满足这一要求。本文还将评估所提出的融合方法所涉及的计算挑战。
This paper will focus on the process of 'fusing' several observations or models of uncertainty into a single resultant model. Many existing approaches to fusion use subjective quantities such as 'strengths of belief' and process these quantities with heuristic algorithms. This paper argues in favor of quantities that can be objectively measured, as opposed to the subjective 'strength of belief' values. This paper will focus on probability distributions, and more importantly, structures that denote sets of probability distributions known as 'credal sets'. The novel aspect of this paper will be a taxonomy of models of fusion that use specific types of credal sets, namely probability interval distributions and Dempster-Shafer models. An objective requirement for information fusion algorithms is provided, and is satisfied by all models of fusion presented in this paper. Dempster's rule of combination is shown to not satisfy this requirement. This paper will also assess the computational challenges involved for the proposed fusion approaches.
