A self-contained guide to the CMB Gibbs sampler
  We present a consistent self-contained and pedagogical review of the CMB
Gibbs sampler, focusing on computational methods and code design. We provide an
easy-to-use CMB Gibbs sampler named SLAVE developed in C++ using
object-oriented design. While discussing why the need for a Gibbs sampler is
evident and what the Gibbs sampler can be used for in a cosmological context,
we review in detail the analytical expressions for the conditional probability
densities and discuss the problems of galactic foreground removal and
anisotropic noise. Having demonstrated that SLAVE is a working, usable CMB
Gibbs sampler, we present the algorithm for white noise level estimation. We
then give a short guide on operating SLAVE before introducing the
post-processing utilities for obtaining the best-fit power spectrum using the
Blackwell-Rao estimator.
