Markov Chain Monte Carlo (MCMC) methods are powerful algorithms that enable statisticians to explore information about probability distributions through computer simulations when exact theoretical methods are not feasible. The Gibbs sampler, for example, allows us to gather information about marginal and joint distributions of multivariate densities assuming that we know information about the conditional distributions. Of particular interest is the use of MCMC methods in Bayesian statistics to help estimate posterior distributions.