The four steps of a Bayesian analysis are

1. Specify a joint distribution for the outcome(s) and all the unknowns, which
  typically takes the form of a marginal prior distribution for the unknowns
  multiplied by a likelihood for the outcome(s) conditional on the unknowns.
  This joint distribution is proportional to a posterior distribution of the
  unknowns conditional on the observed data
2. Draw from posterior distribution using Markov Chain Monte Carlo (MCMC).
3. Evaluate how well the model fits the data and possibly revise the model.
4. Draw from the posterior predictive distribution of the outcome(s) given
  interesting values of the predictors in order to visualize how a manipulation
  of a predictor affects (a function of) the outcome(s).
