Directed graphic models based on conditional independence provide a compact and concise representation of an expert's subjective belief about existing relationships between variables. Faced with the task of building a greater model, each expert must be a specialist in some subset of the whole knowledge domain. It would be desirable to aggregate the knowledge provided by those specialists under the form of graphical models into a single and more general representation. This article studies the consensus model that would be obtained by combining two graphs associated with Bayesian networks and applying the union and intersection of their independencies.