When we do not know the structure of the network, we have to do model selection . We learn the structure by generating a model space, and by evaluating the models by using a metric with respect to a database D. We select the model that fits the data best.
One existing metric is the Bayesian Information Criterion: [19]
is the maximum likelihood estimate of in structure Sm. N is the sample size and dim() the dimensionality.
Several other metrics exist for evaluating the network: