Efficient neighborhood selection for Gaussian graphical models
Y Yang, J Etesami, N Kiyavash - arXiv preprint arXiv:1509.06449, 2015 - arxiv.org
Y Yang, J Etesami, N Kiyavash
arXiv preprint arXiv:1509.06449, 2015•arxiv.orgThis 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.
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.
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.
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