Is ensemble modeling a Bayesian approach? I am thinking it like this: our final model(posterior) is based on other primary models(prior). Can you guys give your opinions?
The question is probably better suited on CrossValidated, but I'll give you a hint.
The way you describe it, Bayesian approach does not fit directly, because Bayes theorem states that posterior equals prior times the likelihood (normalized). The final ensemble model is a weighted sum of individual models. It's not clear what you consider a likelihood to make an ensemble Bayesian.
If you are looking for a probabilistic interpretation, here's a better one: the ensemble model represents a joint distribution of the model selector variable (what is the probability that a particular model is good for a given input) and the model distribution (the accuracy of a particular model). The better you pick both of these distributions (proper models and their weights), the better the ensemble.