A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

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, and s...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 30; no. 4; pp. 713 - 724
Main Author 徐俊刚 赵越 陈健 韩超
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2015
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Bayesian analysis
Compounds
Computer networks
Computer Science
Conditional probability
Constrictions
Data Structures and Information Theory
Heuristic
Heuristic methods
Information Systems Applications (incl.Internet)
Knowledge
Learning
Machine learning
Markov analysis
Markov chains
MCMC算法
Parents
Polynomials
Probability distribution
R&D
Regular Paper
Research & development
Restrictions
Software Engineering
Stochastic models
Studies
Subject specialists
Tasks
Theory of Computation
先验知识
启发式算法
多项式时间
结构学习算法
网络结构
贝叶斯网络
边缘概率分布
United States > US
prior knowledge
Bayesian network
Markov chain Monte Carlo
structure learning
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Summary: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, and 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 and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose 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.
Bibliography:Jun-Gang Xu , Yue Zhao ,Jian Chen, Chao Han （1School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China; 2School of Software Engineering, South China University of Technology, Guangzhou 510006, China）
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, and 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 and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose 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.
11-2296/TP
Bayesian network, structure learning, Markov chain Monte Carlo, prior knowledge
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SourceType-Scholarly Journals-1
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-015-1556-8