MCMC SAMPLES SELECTING FOR ONLINE BAYESIAN NETWORK STRUCTURE LEARNING

摘要：

This paper presents an online learning algorithm for Bayesian network structure, which adopts Important Sampling method of Markov Chain Monte Carlo for online samples evaluation and proper model structure selecting combined with probability distribution of a former. It selects a set of optimized samples for online learning and adjusting based on an existing reliable model structure. And then it learns and adjusts structure online using an important samples set. At last it evaluates the obtained structure by model evaluation and select a reliable one as a new structure. The algorithm proposed in this paper reduces the calculating loads by important samples instead of all samples and implements structure learning online. The experiment shows that the algorithm in this paper can achieve online structure learning and it also has a preferable precision and convergence rapidly.

关键词： Bayesian network Online structure learning MCMC

作者: SHAO-ZHONG ZHANG LU LIU

作者单位: Department of Information Systems, School of Economics of Management, Beihang University, Beijing, 1 Department of Information Systems, School of Economics of Management, Beihang University, Beijing, 1

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1762-1767

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）