I have a poster at SETAC Copenhagen about Bayesian networks. My main message is that Bayesian networks should not be limited to specific applications of probabilistic graphical models, and that the term is currently confusing including both expert informed Bayesian Belief networks and data-driven Bayesian networks in machine learning. I do not see any reason why not Bayesian statistical models can not be seen as a Bayesian network. Widening the concept can benefit from the increasing uptake and positive response of Bayesian networks to support the need for flexible quantitative models in science and society. With this in mind, there are different modelling approaches able to do different things that are Bayesian networks. We then have to go back and describe what each approach actually does. What do I gain from this? Well. at least I am better off when we include any probabilistic graphical modelling into the concept of Bayesian network.
Check out my poster and talk part of the survey giving your perspective on what is (or is not) a Bayesian network.
The survey – link to google forms
Results can be seen here