经典概率论(二):扩展概率论的代数运算

IF 0.6
Guoyin Wang
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引用次数: 0

摘要

本文的第二部分给出了一般概率论扩展数学结构上的一组综合代数算子。人们认识到,经典的概率论是在一组高度耦合的操作中循环定义的。为了解决这一基本问题,引入了一般概率论的简化框架。发现对连续事件的条件概率运算是独立操纵其他概率运算的关键。这导致了对一般概率的严格操作的重新审视框架。这也为贝叶斯定律在基本概率论中更一般的变样本空间和复杂事件关系的应用提供了证明。重新审视的概率论使得在认知信息学、计算智能、认知机器人、复杂系统、软计算和脑信息学等当代领域对形式推理、定性、量化和语义分析中的不确定性事件和因果关系进行严格的处理成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classic Probability Revisited (II): Algebraic Operations of the Extended Probability Theory
Part II of this paper presents a set of comprehensive algebraic operators on the extended mathematical structures of the general probability theory. It is recognized that the classic probability theory is cyclically defined among a small set of highly coupled operations. In order to solve this fundamental problem, a reductive framework of the general probability theory is introduced. It is found that conditional probability operation on consecutive events is the key to independently manipulate other probability operations. This leads to a revisited framework of rigorous manipulations on general probabilities. It also provides a proof for a revisited Bayes’ law fitting in more general contexts of variant sample spaces and complex event relations in fundamental probability theories. The revisited probability theory enables a rigorous treatment of uncertainty events and causations in formal inference, qualification, quantification, and semantic analysis in contemporary fields such as cognitive informatics, computational intelligence, cognitive robots, complex systems, soft computing, and brain informatics.
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