%0 Journal Article
%T MCMC approach to Bayesian networks learning<br>Bayes网络学习的MCMC方法
%A YUE Bo
%A JIAO Li-cheng
%A <br>岳 博
%A 焦李成
%J 控制理论与应用
%D 2003
%I 
%X 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.
%K Bayesian networks
%K Markov chain Monte Carlo
%K model selection
%K stochastic search<br>Bayes网络
%K Markov链Monte
%K Carlo方法
%K 模型选择
%K 随机搜索
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=F6E13CE5D1206CFE&yid=D43C4A19B2EE3C0A&vid=A04140E723CB732E&iid=E158A972A605785F&sid=90773C2285A2F0BB&eid=E04FC1B5BC47587B&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=3&reference_num=7