The interpretation of the probability is the same as in frequentist statistics, except you're making statements about the model resulting from your assumptions and data, instead of some hypothetical experiment. I suppose the Bayesian approach is more about building the model whereas the frequentist approach is more about selecting the best model out of several.
>The interpretation of the probability is the same as in frequentist statistics
Not at all. Frequentists cannot define a probability on whether it will rain in a location on a given day. They will respond that such a probability is meaningless. Bayesians can, however, give a meaning to it.
True, but the way a Bayesianist (?) will assign meaning to it involves creating a model, based on some assumptions, which will return a probability. The Bayesian notion of probability is equivalent to the frequentist notion of probability for experiments done on that model. In that sense they are the same.
