  This paper addresses the problem of neighborhood selection for Gaussian
graphical models. We present two heuristic algorithms: a forward-backward
greedy algorithm for general Gaussian graphical models based on mutual
information test, and a threshold-based algorithm for walk summable Gaussian
graphical models. Both algorithms are shown to be structurally consistent, and
efficient. Numerical results show that both algorithms work very well.
