引用本文:岳 博,焦李成.Bayes网络学习的MCMC方法[J].控制理论与应用,2003,20(4):582~584.[点击复制]
YUE Bo,JIAO Li-cheng.MCMC approach to Bayesian networks learning[J].Control Theory and Technology,2003,20(4):582~584.[点击复制]
Bayes网络学习的MCMC方法
MCMC approach to Bayesian networks learning
摘要点击 2978  全文点击 2031  投稿时间：2001-09-24  修订日期：2002-06-17
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/j.issn.1000-8152.2003.4.021
  2003,20(4):582-584
中文关键词  Bayes网络  Markov链Monte Carlo方法  模型选择  随机搜索
英文关键词  Bayesian networks  Markov chain Monte Carlo  model selection  stochastic search
基金项目  国家自然科学基金(60073053).
作者单位E-mail
岳 博 西安电子科技大学 雷达信号处理国家重点实验室, 陕西 西安 710071 yuebo@rsp.xidian.edu.cn 
焦李成 西安电子科技大学 雷达信号处理国家重点实验室, 陕西 西安 710071  
中文摘要
      基于Bayes统计理论, 提出了一种从数据样本中学习Bayes网络的Markov链Monte Carlo(MCMC)方法. 首先通过先验概率和数据样本的结合得到未归一化的后验概率, 然后使用此后验概率指导随机搜索算法寻找“好”的网络结构模型. 通过对Alarm网络的学习表明了本算法具有较好的性能.
英文摘要
      A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. In many cases, the authors hoped to learn Bayesian networks from data. Using the Markov chain Monte Carlo (MCMC) approach, this paper proposed a Bayesian statistical method for learning Bayesian networks from data, in terms of network structures and parameters. Prior specification and stochastic search were two important components of this approach. The combination of prior probability and data samples induced a posterior distribution that would guide the stochastic search towards the network structures having the maximal posterior probability. The performance of this approach is illustrated by the learning of the Alarm network from data.