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.

引用

页码：391 / 396

页数：6

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