Learning Bayesian network parameters from small data sets: a further constrained qualitatively maximum a posteriori method

DOI10.1016/J.IJAR.2017.08.009zbMath1419.68075OpenAlexW2751326998MaRDI QIDQ1679643

Zhi-gao Guo, Ruo-hai Di, Xiao-guang Gao, Hao Ren, Yu Yang, Da-Qing Chen

Publication date: 21 November 2017

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: http://researchopen.lsbu.ac.uk/1441/1/IJAR-David-V10.pdf

zbMATH Keywords

domain knowledgeBayesian networkparameter learningsmall data sets

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Hierarchical two-part MDL code for multinomial distributions ⋮ Learning Bayesian network parameters via minimax algorithm

Uses Software

• NESTOR

Cites Work

• Unnamed Item
• The sample complexity of learning fixed-structure Bayesian networks
• Learning Bayesian network parameters under order constraints
• Fundamental concepts of qualitative probabilistic networks
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• On Information and Sufficiency

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