# Correct model specification and pre-specification: Is this problem solved in Bayesian statistics?

In frequentist statistics, the validity of the inference depends on the assumption that the model is correctly specified as well as pre-specified. Violations of these assumptions (i.e. we only specify a linear relationship but the true relationship is non-linear or we perform model selection procedures) undermine the validity of inference. Are there similar assumptions in Bayesian statistics or how are they solved in Bayesian statistics? I assume the prior is our belief about the correct model and therefore there is no misspecification?

• I just found this comment from Andrew Gelman, which seems to discuss my problem but I still would appreciate some more formal explanations: andrewgelman.com/2013/03/14/… – Stats_Monkey Nov 9 '17 at 17:14