Learning Bayesian networks from datasets joining continuous and discrete variables

The second problem is the control of the type I error level. Usually, learning algorithms output a network that is the best according to some optimization criteria, but the reliability of particular relationships represented by this network is unknown. We address this problem by allowing the user to specify the expected error level and adjusting the parameters of the scoring criteria to this level.