[1]ZHAO Sengfeng,ZHAO Keqin.The principle of a connection number in probability and its application in probabilistic reasoning[J].CAAI Transactions on Intelligent Systems,2012,7(3):200-205.
Copy

The principle of a connection number in probability and its application in probabilistic reasoning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 3 Page number: 200-205 Column: 学术论文—人工智能基础 Public date: 2012-06-25
Title:
The principle of a connection number in probability and its application in probabilistic reasoning
Author(s):
ZHAO Sengfeng1;  ZHAO Keqin2; 3
1.Department of Science, Zhijiang College of Zhejiang University of Technology, Hangzhou 310024, China;
2.Zhuji Institute of Connection Mathematics, Zhuji 311811, China;
3.Center for NonTraditional Security and Peaceful Development Studies, Zhejiang University, Hangzhou 310058, China
Keywords:
probabilistic reasoning;  random events;  connection number;  main events;  adjoining events;  connection probability (complex probability);  negative probability
CLC:
TP18
DOI:
-
Abstract:
For more objective probabilistic reasoning, a new probability theory was created in this paper. Through exploring the nature of randomness of some simple randomized trials, it can be known that the randomness of events has two interlinked attributes. The two random events A and 〖AKA-〗 always exist in a pair, and these random events can be divided into main events and adjoining events. Their respective probabilities are main probability (large number probability) and adjoining probability (sudden probability). A connection number was used to describe them. The probability of the connection number was called connection probability (complex probability). The i in the connection probability is a key parameter for the transformation of main events and adjoining events in the random experiment. The meaning of negative probability was explained, the application of connection probability in probabilistic reasoning was also illustrated in this paper.
References:
［1］李德毅．不确定性人工智能［M］．北京：国防工业出版社, 2005: 1400.
［2］王梓坤．概率论基础及其应用［M］．北京：科学出版社, 1979: 218219.
［3］赵克勤．集对分析及其初步应用［M］．杭州：浙江科技出版社, 2000: 4464.
［4］赵克勤，宣爱理．集对论——一种新的不确定性理论方法与应用［J］．系统工程, 1996, 14(1): 1823.
ZHAO Keqin, XUAN Aili. Set pair theory—a new theory method of nondefine and its applications［J］. Systems Engineering, 1996, 14(1): 1823.
［5］赵克勤．集对分析的不确定性理论在AI中的应用［J］．智能系统学报, 2006, 1(2): 1625.
?ZHAO Keqin. The application of uncertainty systems theory of set pair analysis（SPA）in the artificial intelligence［J］. CAAI Transactions on Intelligent Systems, 2006, 1(2): 1625.
［6］王文圣，金菊良，丁晶，等．水资源系统评价新方法——集对评价法［J］．中国科学E辑：技术科学, 2009, 39(9): 15291534.
WANG Wensheng, JIN Juliang, DING Jing, et al. A new approach to water resources system assessment—set pair analysis method［J］. Science in China, Series E: Technological Sciences, 2009, 39(9): 15291534.
［7］ZHOU Ze’nan. Decision support system based on set pair analysis and its application［J］. Engineering Sciences, 2007, 5(3): 7681.
［8］GAO Feng, CHEN Yingwu. A project risk ranking approach based on set pair analysis［J］. Engineering Sciences, 2006, 4(3): 8993.
［9］沈定珠．体育用联系数学［M］．深圳：中国教育文化出版社, 2007: 1191.
［10］王文圣，李跃清，金菊良，等．水文水资源集对分析［M］．北京：科学出版社, 2010: 1183.
［11］米虹，赵克勤．非传统安全集对分析［M］．北京：知识产权出版社, 2010: 1218.
［12］刘保相．粗糙集对分析决策模型与应用［M］．北京：科学出版社, 2010: 1185.
［13］郭瑞林．作物育种同异理论与方法［M］．北京：中国农业科学技术出版社, 2011: 1294.
［14］赵克勤．试论集对分析与概率论的关系［C］//数学及其应用文集．长沙：湖南科技出版社, 1995: 253.
［15］赵克勤．二元联系数A+Bi的理论基础与基本算法及在人工智〖CM5*1/3-5〗能中的应用［J］. 智能系统学报, 2008, 3(6): 476486.
ZHAO Keqin. The theoretical basis and basic algorithm of binary connection A+Bi and its application in AI［J］. CAAI Transactions on Intelligent Systems, 2008, 3(6): 476486.
［16］赵秀恒，米立民．概率论与数理统计［M］．北京：高等教育出版社, 2008: 128.
［17］蔡自兴，徐光佑．人工智能及其应用［M］．北京：清华大学出版社, 2010: 114116.
Similar References:

Memo

-

Last Update: 2012-09-05

Copyright © CAAI Transactions on Intelligent Systems