Learning Bayesian belief networks based on the minimum description length principle: Basic properties

被引：0

作者：

Suzuki, J ^{[1
]}

机构：

[1] Osaka Univ, Fac Sci, Osaka 5600043, Japan

来源：

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 1999年 / E82A卷 / 10期

关键词：

minimum description length principle; Bayesian belief network; Chow and Liu algorithm; Cooper and Herskovits procedure; MDL-based procedure; stochastic rule learning;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approaches (MDL and Bayesian), and recommend a class of priors from which the formula is obtained. Finally, we show as a merit of using the formula that a modified version of the Chow and Liu algorithm is obtained. The modified algorithm finds a set of trees rather than a spanning tree based on the MDL principle.

引用

页码：2237 / 2245

页数：9

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