  In this paper we propose a two-level hierarchical Bayesian model and an
annealing schedule to re-enable the noise variance learning capability of the
fast marginalized Sparse Bayesian Learning Algorithms. The performance such as
NMSE and F-measure can be greatly improved due to the annealing technique. This
algorithm tends to produce the most sparse solution under moderate SNR
scenarios and can outperform most concurrent SBL algorithms while pertains
small computational load.
