Why joint probability in generative models? I have been reading about generative models for past few days and there's one question that's bugging me. Many sources(Wiki, Google dev article) mention that generative models try to model the joint probability distribution p(x, y) and that to generate new samples we sample from this distribution. My question is why model and sample from the joint distribution and not the conditional distribution p(x|y)?
Its because joint distribution takes all of the values variable Y can take and conditional only one. Additionally it could be easier to compute/use approximative methods for joint.