Bayesian data mining, with application to benchmarking and credit scoring

被引：26
|
作者：
Giudici, P [1 ]
机构：
[1] Univ Pavia, Dept Econ & Quantitat Methods, I-27100 Pavia, Italy
来源：
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2001年 / 17卷 / 01期
关键词：
Bayesian model selection; credit scoring; financial benchmarking; graphical models; Markov chain Monte Carlo methods;
D O I：
10.1002/asmb.425
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
The purpose of this article is to show that Bayesian methods, coupled with Markov chain Monte Carlo computational techniques, can be successfully employed in the analysis of highly dimensional complex datasets, such as those occurring in data mining applications. Our methodology employs conditional independence graphs to localize model specification and inferences, thus allowing a considerable gain in flexibility of modelling and efficiency of the computations. Copyright (C) 2001 John Wiley & Sons, Ltd.
引用
收藏
页码：69 / 81
页数：13
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