否定
度量(数据仓库)
事件(粒子物理)
概率测度
登普斯特-沙弗理论
概率逻辑
概率论
条件概率
数学
概率分布
应用概率
计算机科学
人工智能
统计
数据挖掘
量子力学
物理
程序设计语言
作者
Ronald Fagin,Joseph Y. Halpern
出处
期刊:International Joint Conference on Artificial Intelligence
日期:1989-08-20
卷期号:: 1161-1167
被引量:82
摘要
We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. Thus, the measure of belief in an event can be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a precise sense to Dempster-Shafer belief functions. Hence, in addition to providing promising new conceptual tools for dealing with uncertainty, our approach shows that a key part of the important Dempster-Shafer theory of evidence is firmly rooted in classical probability theory.
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