[1]赵克勤,赵森烽.贝叶斯概率向赵森烽-克勤概率的转换与应用[J].智能系统学报,2015,10(01):51-61.[doi:10.3969/j.issn.1673-4785.201405022]
 ZHAO Keqin,ZHAO Senfeng.Bayes probability transition to Zhao Senfeng-Keqin probability and its application[J].CAAI Transactions on Intelligent Systems,2015,10(01):51-61.[doi:10.3969/j.issn.1673-4785.201405022]
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贝叶斯概率向赵森烽-克勤概率的转换与应用(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
51-61
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Bayes probability transition to Zhao Senfeng-Keqin probability and its application
作者:
赵克勤12 赵森烽3
1. 浙江大学 非传统安全与和平发展研究中心集对分析研究所, 浙江 杭州 310058;
2. 诸暨市联系数学研究所, 浙江 诸暨 311811;
3. 浙江工业大学 之江学院, 浙江 杭州 310024
Author(s):
ZHAO Keqin12 ZHAO Senfeng3
1. Center for Non-traditional Security and Peaceful Development Studies, Zhejiang University, Hangzhou 310058, China;
2. Zhuji Institute of Connection Mathematics, Zhuji 311811;
3. School of zhi jiang, Zhejiang Technology University, Hangzhou, 310024, China
关键词:
贝叶斯概率赵森烽-克勤概率联系数后验值智脑思维特性集对分析
Keywords:
Bayes probabilityZhao Senfeng-Keqin probabilityconnection numberposterior valueswisdom brain thinking characteristicsset pair analysis
DOI:
10.3969/j.issn.1673-4785.201405022
文献标志码:
A
摘要:
为研究贝叶斯概率与其后验概率的联系与转化以及联系数化后的贝叶斯推理,定义了贝叶斯概型的赵森烽-克勤概率,其数学形式等同于古典概型、几何概型、频率概型的赵森烽-克勤概率,借助赵森烽-克勤概率中随机转换器i的作用,把贝叶斯概率的后验概率分为增益型、衰减型、维持型,在此基础上给出贝叶斯概率向赵森烽-克勤概率转换定理与相应算法,举例说明贝叶斯概型的赵森烽-克勤概率具有智脑思维的完整性、前瞻性和灵活性等特点,从而为人工智能和其他领域应用贝叶斯推理开辟出一条新途径。
Abstract:
In order to study the Bayesian probability and posterior Bayesian inference relation and transformation as well as the number of contact probability after,The definition of Zhao Senfeng-Keqin probability of Bayes probability model,Zhao Senfeng-Keqin probability of its mathematical form equivalent to classical subscheme, geometric probability, frequency probability model,With the help of Zhao Senfeng-Keqin probability random converter I effect,The Bayesian posterior probability for gain, attenuation, maintenance,Based on this Bayesian probability transformation theorem and the corresponding algorithm to Zhao Senfeng-Keqin probability,To illustrate the characteristics of Bayesian probability model Zhao Senfeng Keqin probability with zhinao thinking integrity, foresight and flexibility etc,open up a new way for the application of artificial intelligence and other areas of Bayesian reasoning.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-5-18;改回日期:。
基金项目:国家社会科学基金重点资助项目(08ASH006);教育部哲学社会科学研究重大课题攻关项目(08JZD0021-D).
作者简介:赵克勤,男,1950年生,浙江省诸暨市联系数学研究所研究员,浙江大学非传统安全与和平发展中心集对分析研究所所长,中国人工智能学会理事,人工智能基础专业委员会副主任,集对分析联系数学专业筹备委员会主任,1989年提出集对分析(联系数学),已出版《集对分析及其初步应用》专著一部,发表学术论文90余篇;赵森烽,男,1993年生,主要研究方向为概率统计、集对分析联系数学等,发表学术论文5篇。
通讯作者:赵克勤.E-mail:zjzhaok@sohu.com.
更新日期/Last Update: 2015-06-16