Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties

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概要

• 論文の詳細を見る

• 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 ratlaer than a spanning tree based on the MDL principle.

• 一般社団法人電子情報通信学会の論文
• 1999-10-25

著者

• Suzuki J

  Osaka Univ. Toyonaka‐shi Jpn

• Suzuki Joe

  Faculty Of Science Osaka University

関連論文

• Some Notes on Universal Noiseless Coding
• Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties

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