In model selection, one typically selects a "best" model from a set of candidate models, and then uses this selected "best" model for prediction.

Unlike a single optimal model, Bayesian model averaging assigns weights to each model and performs weighted averaging to determine the final prediction value. The weight assigned to a model is the posterior probability of the model.