A discrete Bayesian network via bnlearn

Bayesian networks are probabilistic graphical models used for understanding how different variables interact with each other. They are built by exploiting the conditional dependencies of each variable using Bayesian theory. For example, let's assume that we have three variables: sleep quality, diet quality, and work performance. For the sake of simplicity, let's also assume that each variable can only take two values: high and low. In our usual regression or classification framework, we would model one of these variables in terms of all the rest. Of course, we would need to take care to choose a dependent variable that is caused by the covariates in some way (in order to make an inference in a regression context, causality needs to flow from the covariates to the dependent variable). BNs operate differently, and in this case, we...