Improved Gibbs samplers for cosmic microwave background power spectrum estimation

We study different variants of the Gibbs sampler algorithm from the perspective of their applicability to the estimation of power spectra of the cosmic microwave background (CMB) anisotropies. These include approaches studied earlier in the CMB literature as well as new ones which are proposed in this work. We demonstrate all these variants on full- and cut-sky simulations and compare their performance, assessing both their computational and statistical efficiency. For this we employ a consistent comparison metric, an effective sample size per second, commonly used in this context in the statistical literature. We show that one of the proposed approaches, referred to as Centered overrelax, which capitalizes on additional, auxiliary variables to minimize the computational time needed per sample, uses overrelaxation to decorrelate subsequent samples, and performs better than the standard Gibbs sampler by a factor between one and two orders of magnitude in the nearly full-sky, satellitelike cases. It therefore potentially provides an interesting alternative to the currently favored approaches.