As the combination of parameter learning and structure learning, learning Bayesian networks can also be examined, Parameter learning is estimation of the dependencies in the network. Structural learning is the estimation of the links of the network. In terms of whether the structure of the network is known and whether the variables are all observable, there are four types of learning Bayesian networks cases. In this paper, first introduce two cases of learning Bayesian networks from complete data: known structure and unobservable variables and unknown structure and unobservable variables. Next, we study two cases of learning Bayesian networks from incomplete data: known network structure and unobservable variables, unknown network structure and unobservable variables.
Digital Object Identifier (DOI)
"Study of Four Types of Learning Bayesian Networks Cases,"
Applied Mathematics & Information Sciences: Vol. 08:
1, Article 47.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss1/47