METHOD OF PROBABILISTIC INFERENCE FROM LEARNING DATA IN BAYESIAN NETWORKS

被引：7
|
作者：
Terent'yev, A. N. [1 ]
Biduk, P. I. [1 ]
机构：
[1] Natl Tech Univ Ukraine, Kyiv Polytech Inst, Inst Appl Syst Anal, Kiev, Ukraine
来源：
CYBERNETICS AND SYSTEMS ANALYSIS | 2007年 / 43卷 / 03期
关键词：
Bayesian network; conditional probability tables; probabilistic inference; computational characteristics;
D O I：
10.1007/s10559-007-0061-7
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Bayesian networks (BN) are a powerful tool for various data-mining systems. The available methods of probabilistic inference from learning data have shortcomings such as high computation complexity and cumulative error. This is due to a partial loss of information in transition from empiric information to conditional probability tables. The paper presents a new simple and exact algorithm for probabilistic inference in BN from learning data.
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页码：391 / 396
页数：6
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