Abstract
There are different structure of the network and the variables, and the process of learning Bayesian networks has a lot of different forms. The structure of the network can be unknown or known, and the variables can be observable or hidden in some or all of the data points. Consequently, there are four cases of learning Bayesian networks from data: known structure and observable variables, unknown structure and observable variables, known structure and unobservable variables and unknown structure and unobservable variables. In this paper, we focus on known structure and observable variables, unknown structure and observable variables.
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