Title :
Study of the Case of Learning Bayesian Network from Incomplete Data
Author_Institution :
Sch. of Econ. & Manage., Henan Inst. of Sci. & Technol., Xinxiang, China
Abstract :
Learning Bayesian networks can be examined as the combination of parameter learning and structure learning. Parameter learning is estimation of the conditional probabilities (dependencies) in the network. Structural learning is the estimation of the topology (links) of the network. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data. In this paper, we introduce two cases of learning Bayesian networks from complete data: known structure and unobservable variables and unknown structure and unobservable variables.
Keywords :
Bayes methods; belief networks; probability; conditional probability; learning Bayesian network; parameter learning; structure learning; Bayesian methods; Conference management; Engineering management; Industrial engineering; Inference algorithms; Information management; Innovation management; Iterative algorithms; Sampling methods; Technology management; Bayesian networks; Network Structure; Observable Variables;
Conference_Titel :
Information Management, Innovation Management and Industrial Engineering, 2009 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-0-7695-3876-1
DOI :
10.1109/ICIII.2009.478
