Probability consistent models. Model building

We deal with the problem we can face when building pprobability models to represent the reality being observed. We discuss what conditions lead to under, over and exact model definitions. In particular, we warn the reader about specifying inconsistent conditions, because no model can satisfy them. Finally, we recommend Bayesian networks, as the better way to define models and provide some illustrative examples.