I have posted the codes of T-SBL and T-MSBL on my homepage, which are developed in my paper:
Zhilin Zhang, Bhaskar D. Rao, Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning, IEEE Journal of Selected Topics in Signal Processing, Special Issue on Adaptive Sparse Representation of Data and Applications in Signal and Image Processing, 2011, accepted.
They can be downloaded from here. But before you running/testing the codes, I strongly suggest you spend 3 minutes in reading the short cookbook on how to set suitable input parameters. There are four study cases, such as noiseless case, mild noisy case, strongly noisy case, and so on. You can check the example in each case for advice on choosing parameters. But don't be scared. Generally, in any case you only need to set 1 or 2 parameters, and setting them is a piece of cake (you do not need to understand SBL or read any SBL paper!)
Along with the codes of T-SBL and T-MSBL, there are several demos, which can re-produce the experiment results in my paper.
The paper has been revised. But it is still not the final version. I am now revising the language :( The final version will be uploaded at the end of this month).
Also, I've uploaded the code of M-SBL. It can be downloaded from here. Yes, David has a code of M-SBL. But his code is not suitable for noisy cases and even not suitable for noiseless case. I also suggest you to read the comments in my code to correctly use M-SBL for algorithm comparison.