条件随机量下两个条件事件的广义合取与析取

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lydia Castronovo , Giuseppe Sanfilippo
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引用次数: 0

摘要

近年来,在相干性的背景下,深化了两个条件事件作为满足基本概率性质的条件随机量的合取和析取的概念。在该框架中,将两个条件事件的合取和析取定义为五值对象,其中五值对象为两个条件事件(主观)赋值概率的值。在本文中,我们提出了这些结构的推广,其中这些新对象不是依赖于两个条件事件的概率,而是依赖于单位区间内的两个任意值a,b。我们用广义的德摩根定律证明了它们之间的联系,并通过几何方法计算了这些新对象在精确和不精确情况下的下界和上界。此外,还分析了a和b的特定值或某些逻辑关系下的一些特殊情况。本文的结果表明,满足事件间运算所有有效的逻辑和概率性质的对象是依赖于两个条件事件的概率的对象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized conjunction and disjunction of two conditional events in the setting of conditional random quantities
In recent papers, notions of conjunction and disjunction of two conditional events as suitable conditional random quantities, which satisfy basic probabilistic properties, have been deepened in the setting of coherence. In this framework, the conjunction and the disjunction of two conditional events are defined as five-valued objects, among which are the values of the (subjectively) assigned probabilities of the two conditional events. In the present paper we propose a generalization of these structures, where these new objects, instead of depending on the probabilities of the two conditional events, depend on two arbitrary values a,b in the unit interval. We show that they are connected by a generalized version of the De Morgan's law and, by means of a geometrical approach, we compute the lower and upper bounds on these new objects both in the precise and the imprecise case. Moreover, some particular cases, obtained for specific values of a and b or in case of some logical relations, are analyzed. The results of this paper lead to the conclusion that the only objects satisfying all the logical and the probabilistic properties already valid for the operations between events are the ones depending on the probabilities of the two conditional events.
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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