Using Bayesian belief networks to evaluate credit guarantee-risk

被引：0
|
作者：
He, ZY [1 ]
Han, YQ [1 ]
Wang, HW [1 ]
Mei, Q [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China
来源：
2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS | 2002年
关键词：
bayesian belief networks (BBN); risk evaluation; credit guarantee risk (CGR);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The evaluation of credit, guarantee risk (herein after called CGR) involves many complex problems such as how to quantify risk, how to express causal relationships of risk factors, how to fuse all kinds of information and evaluate risk with uncertainties. So the evaluation of CGR is very difficult because of its uncertainty and diverse information. Bayesian belief networks (herein after called BBN) are basically a framework for reasoning uncertainty and diverse information. This paper presents BBN as a new methodology to evaluate CGR. An example is presented by this method.
引用
收藏
页码：973 / 977
页数：5
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