Title :
Bayesian network structure learning for discrete and continuous variables
Author_Institution :
Dept. of Math., Osaka Univ., Suita, Japan
Abstract :
We consider estimation of Bayesian network structures given a finite number of examples when both discrete and continuous random variables are present in a Bayesian network.
Keywords :
Bayes methods; belief networks; learning (artificial intelligence); random processes; Bayesian network structure estimation; Bayesian network structure learning; continuous random variable; discrete variable; Artificial neural networks; Bayesian methods; Cognition; Density functional theory; Estimation; Mathematical model; Random variables; Bayesian networks; discrete/continuous variables; model selection; structure estimation;
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
Conference_Location :
Jalarta
Print_ISBN :
978-1-4673-1459-6
DOI :
10.1109/URKE.2012.6319529
