Gibbs Sampler

Gibbs Sampler is an algorithm used to generate marginal distribution from the conditional ones. It is a special case of the Metropolis-Hastings algorithm. In this post I just want to respond to Subbiah's suggestion in the comment on my post on Metropolis-Hastings algorithm. He suggests a paper by George Casella and Edward George, which explains in more or less simple terms the Gibbs Sampler. I really liked the paper more than the one I mentioned about the Metropolis-Hastings algorithm, as it is much more intuitive. Sections 2 and 3, which shows how the algorithm works in practice, are quite interesting and written in a intelligent and clear way.The paper does not go into Bayesian statistics, it is more about showing how the Markov Chain works on converging to the desired marginal distribution. It seems to me that the paper is very helpful if you are planning to learn Bayesian Statistics, in which case it will serve well as a pre-requisite for the chapters on simulation from the posteriori.