Getting Started: MCMC Procedure

There are three examples in this "Getting Started" section: a simple linear regression, the Behrens-Fisher estimation problem, and a random effects model. The regression model is chosen for its simplicity; the Behrens-Fisher problem illustrates some advantages of the Bayesian approach; and the random effects model is one of the most prevalently used models.

Keep in mind that PARMS statements declare the parameters in the model, PRIOR statements declare the prior distributions, and MODEL statements declare the likelihood for the data. In most cases, you do not need to supply initial values. The procedure advises you if it is unable to generate starting values for the Markov chain.