Traditionally, you choose a model and perform analysis based on this model. The results are conditional on the chosen model. In the presence of multiple plausible models, this approach may not be reliable
Model averaging allows you to perform analysis based on multiple models and thus account for model uncertainty in the results. BMA accounts for model uncertainty according to Bayesian principles, which can be applied universally to any data analysis. In the regression serving, model uncertainty describes the uncertainty about which predictors should be included in a regression model.
The new command bmaregress performs BMA for linear regression and can be used for inference, prediction, and, if desired, even model selection. For instance,(bmaregress y x1 x2 ) considers all four possible models for outcome y that include or exclude predictors x1 and x2 and combines these models according to how likely each model is based on the observed data. You can choose from a variety of prior distributions to explore the effect of assumptions about a model's and predictors' importance on the results.
Postestimation commands allow you to estimate the probability of a model, identify important predictors, explore model complexity, obtain predictive means, evaluate predictive performance, and perform inference on regression coefficients.