A self-contained guide to the CMB Gibbs sampler

Understand

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.