贝叶斯概率向赵森烽-克勤概率的转换与应用

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Title:

Bayes probability transition to Zhao Senfeng-Keqin probability and its application

作者:

赵克勤^1,2, 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 Keqin^1,2, 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 probability; Zhao Senfeng-Keqin probability; connection number; posterior values; wisdom brain thinking characteristics; set 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.