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›› 2015,Vol. 30 ›› Issue (4): 713-724.

• Special Section on Selected Paper from NPC 2011 •

### 一种基于先验知识的贝叶斯网结构学习算法

Jun-Gang Xu1(徐俊刚), Member, CCF, ACM, IEEE, Yue Zhao1(赵越), Jian Chen2(陈健), Member, CCF, ACM, IEEE, Chao Han2(韩超)

1. 1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China;
2. School of Software Engineering, South China University of Technology, Guangzhou 510006, China
• 收稿日期:2015-01-31 修回日期:2015-03-25 出版日期:2015-07-05 发布日期:2015-07-05
• 作者简介:Jun-Gang Xu is an associate professor of the School of Computer and Control Engineering (SCCE) at University of Chinese Academy of Sciences (UCAS), Beijing, where he joined as a faculty member in 2005. He received his B.S. and M.S. degrees in mechanical engineering from Southwest Petroleum University, Chengdu, and Shandong University, Jinan, in 1996 and 1999 respectively, and his Ph.D. degree in computer science from Graduate University of Chinese Academy of Sciences, Beijing, in 2003. During 2003~2005, he was a postdoctoral researcher in Tsinghua University, Beijing. His current research interests include data mining, information retrieval, big data management and deep learning.
• 基金资助:

This work was supported by the National Natural Science Foundation of China under Grant No. 61372171 and the National Key Technology Research and Development Program of China under Grant No. 2012BAH23B03.

### A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

Jun-Gang Xu1(徐俊刚), Member, CCF, ACM, IEEE, Yue Zhao1(赵越), Jian Chen2(陈健), Member, CCF, ACM, IEEE, Chao Han2(韩超)

1. 1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China;
2. School of Software Engineering, South China University of Technology, Guangzhou 510006, China
• Received:2015-01-31 Revised:2015-03-25 Online:2015-07-05 Published:2015-07-05
• About author:Jun-Gang Xu is an associate professor of the School of Computer and Control Engineering (SCCE) at University of Chinese Academy of Sciences (UCAS), Beijing, where he joined as a faculty member in 2005. He received his B.S. and M.S. degrees in mechanical engineering from Southwest Petroleum University, Chengdu, and Shandong University, Jinan, in 1996 and 1999 respectively, and his Ph.D. degree in computer science from Graduate University of Chinese Academy of Sciences, Beijing, in 2003. During 2003~2005, he was a postdoctoral researcher in Tsinghua University, Beijing. His current research interests include data mining, information retrieval, big data management and deep learning.
• Supported by:

This work was supported by the National Natural Science Foundation of China under Grant No. 61372171 and the National Key Technology Research and Development Program of China under Grant No. 2012BAH23B03.

Abstract: Bayesian Network is an important probabilistic graphical model that represents a set of random nodes and their conditional dependencies. Bayesian Networks have been increasingly used in a wide range of applications, such as natural language processing, hardware diagnostics, bioinformatics, statistical physics, econometrics, etc. Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a Non-deterministic Polynomial-time (NP) hard problem. To solve this problem, many heuristic algorithms have been proposed, some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction of prior knowledge, use restriction of prior knowledge, etc. This makes it difficult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into Markov Chain Monte Carlo (MCMC) algorithm and proposed an algorithm called Constrained MCMC (C-MCMC) algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined: existence of parent node, absence of parent node and distribution knowledge including the Conditional Probability Distribution (CPD) of edges and the Probability Distribution (PD) of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

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