Efficient Neighborhood Selection for Gaussian Graphical Models
This addresses the problem of efficient neighborhood selection for Gaussian graphical models, which is incremental as it builds on existing methods with heuristic improvements.
The paper tackled the problem of neighborhood selection for Gaussian graphical models by presenting two heuristic algorithms, a forward-backward greedy algorithm and a threshold-based algorithm, which were shown to be structurally consistent and efficient, with numerical results indicating they 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.