In this talk, we first introduce the multi-task learning problem and the application of Matrix Generalized Inverse Gaussian (MGIG) distribution to the problem. We still propose the GMGIG regression model for multi-task learning. To make the computation tractable, we simultaneously use variational inference and sampling techniques. In particular, we propose two sampling strategies for computing the statistics of the MGIG distribution.