用置信函数处理数据不足时的不确定性推理
Statistical inference with belief functions: A survey
- 基于统计数据推导置信度量，解决数据稀缺问题
- 系统梳理该领域重要研究成果
- 适合关注不确定性建模的研究者参考
置信函数是一种强大的不确定性数学表征框架，尤其适用于数据不足导致无法学习概率分布的情形。基于置信函数的推理链中，第一步是推理：如何从现有数据中学习置信度量。本文聚焦于从统计数据中进行推理，系统回顾该领域的关键贡献。
原文摘要 · Abstract (English)
Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a probability distribution for the problem impractical. The first step in a reasoning chain based on belief functions is inference: how to learn a belief measure from the available data. In this survey we focus, in particular, on making inference from statistical data, and review the most significant contributions in the area.
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