Efficient Neighborhood Selection for Gaussian Graphical Models

Yingxiang Yang; Jalal Etesami; Negar Kiyavash

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arXiv · 1509.06449

Efficient Neighborhood Selection for Gaussian Graphical Models

Yingxiang Yang·Jalal Etesami·Negar Kiyavash

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Abstract

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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Yingxiang Yang, Jalal Etesami, Negar Kiyavash. 2015-09-22. Efficient Neighborhood Selection for Gaussian Graphical Models. https://arxiv.org/abs/1509.06449

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