基于边界的Markov网的发现

摘要：

Markov network ts an another powerful tool besides Bayesian network which can be used to do uncertain inference. A method of learning Markov network automaticly from mass data based on boundary has been discussed in this paper. Taking advantage of an important conclusion in information theory ,we present an efficient boundary based Markov network learning algorithm. This algorithm only demands O(N2) times CI (conditional independence) test. We prove if the joint probability is strictly positive,then the found Markov network must be the minimal I_map of the sample.

关键词：Markov network、Boundary、CI test、I-map

所属期刊栏目：28

分类号：TP393(计算技术、计算机技术)

资助基金：国家自然科学基金69763003

在线出版日期：2004-01-08（万方平台首次上网日期，不代表论文的发表时间）

页数：共6页

页码：78-82,65