A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

• Published in: Journal 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
• Language: English
• 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
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-015-1556-8

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...