The new bayes: prefix command lets you fit Bayesian regression models more easily and fit more models. You could fit a Bayesian linear regression using bayesmh. But now you can fit it by typing
. bayes: regress y x1 x2
That is convenient. What you could not previously do was fit a Bayesian survival model. Now you can with bayes: streg. You can also fit multilevel models with, for instance, bayes: mixed and bayes: melogit. The new bayes: prefix can be used with 45 Stata maximum-likelihood commands.
All of Stata’s Bayesian features are supported by the new bayes: prefix command. You can select from many prior distributions for model parameters or use default priors. You can use the default adaptive Metropolis-Hastings sampling, or Gibbs sampling, or a combination of the two sampling methods, when available.
After estimation, you can use Stata’s standard Bayesian postestimation tools such as bayesgraph to check convergence, bayesstats summary to estimate functions of model parameters, bayesstats ic and bayestest model to compute Bayes factors and compare Bayesian models, and bayestest interval to perform interval hypotheses testing.