Computer Science ›› 2015, Vol. 42 ›› Issue (4): 253-257.doi: 10.11896/j.issn.1002-137X.2015.04.052

Previous Articles     Next Articles

Parameters Learning of Bayesian Networks for Multistate System with Small Sample

XIAO Meng and ZHANG You-peng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: To learn parameters for conditional probability distribution of multi-father nodes,a method was proposed which applys to the multistate nodes in the inaccurate model under the condition of insufficient sample information.Using the assumption of independence of causal interaction,the conditional probability distribution is decomposed and the size of conditional probability table is linear in the numbers of the parent nodes and their states.Using Leaky Noisy-MAX model,the influence of factors not included in the multistate system model can be quantified on the parameters learning.The model parameters extracted from small sample can create conditional probability tables.The results show that the method can improve the efficiency and precision of parameter learning.

Key words: Bayesian networks,Multistate system,Small sample,Independence of causal interaction,Parameters learning

[1] 张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006
[2] Pearl J.Probabilistic Reasoning in Intelligent Systems:Net-works of Plausible Inference[M].San Francisco,CA:Morgan Kaufmann Publishers Inc.,1988:383-408
[3] 王华伟,周经伦,何祖玉,等.基于贝叶斯网络的复杂系统故障诊断[J].计算机集成制造系统,2004,0(2):230-234
[4] 康长青,方磊,华丽,等.基于贝叶斯Noisy Or Gate网络的多传感器目标分类识别[J].计算机测量与控制,2011,9(6):1387-1389
[5] 柴慧敏,王宝树.用于态势估计的一种构造贝叶斯网络参数的方法[J].计算机科学,2006,3(9):140-142
[6] Heckerman D.Causal Independence for Knowledge Acquisition and Inference[C]∥Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (UAI’93).San Mateo:Morgan Kaufmann Publishers Inc.,1993:122-127
[7] Zhang N L,Poole D.Exploiting Causal Independence in Bayesian Network Inference[J].Journal of Artificial Intelligence Research,1996,5:301-328
[8] Díez F J,Galán S F.Efficient Computation for the Noisy-Max[J].International Journal of Intelligent Systems,2004,8(2):165-177
[9] Díez F J,Druzdzel M J.Canonical Probabilistic Models forKnowledge Engineering[R].Technical Report CISIAD-06-01.UNED Madrid,2007
[10] Spirtes P,Glymour C N,Scheines R.Causation,Prediction,and Search[M].Cambridge:The MIT Press,2000
[11] Zagorecki A,Druzdzel M J.Knowledge Engineering for Bayesian Networks:How Common Are Noisy-MAX Distributions in Practice?[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2013,3(1):186-195
[12] Shi D.Extending Noisy-Max Gates to Bidirectional Models[J].Journal of Information & Computational Science,2013,0(13):4085-4096
[13] Zagorecki A,Voortman M,Druzdzel M J.Decomposing LocalProbability Distributions in Bayesian Networks for Improved Inference and Parameter Learning[C]∥Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference(FLAIRS-2006).Menlo Park,CA:AAAI Press,2006:860-865

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!