  We consider the problem of learning underlying tree structure from noisy,
mixed data obtained from a linear model. To achieve this, we use the
expectation maximization algorithm combined with Chow-Liu minimum spanning tree
algorithm. This algorithm is sub-optimal, but has low complexity and is
applicable to model selection problems through any linear model.
