Griddy-Gibbs sampler in a Bayesian hierarchical mixed linear model

We present an example of how one can significantly improve computational efficiency using a Bayesian hierarchical mixed linear model as opposed to modeling without conditioning on random effects. Our example uses non-standard prior distributions and leads to Gibbs conditionals with unknown normalizing constants, so we employ the griddy-Gibbs sampler to facilitate the computations.