In my non expert opinion it is difference in what model tries to predict, at least in some contexts. For example in Reinforcement Learning P(R | A, S), eg. pro

In my non expert opinion it is difference in what model tries to predict, at least in some contexts. For example in Reinforcement Learning P(R | A, S), eg. probability of reward given action and state, while in Bayesian network P(A | B, C), probability of even A given B, C distributions, in such networks you can do inferences. I have no idea how correct or false this explanation is.