• Corpus ID: 52163661

Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points

@article{Bybee2018ChangePointCF,
  title={Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points},
  author={Leland Bybee and Yves F. Atchad{\'e}},
  journal={J. Mach. Learn. Res.},
  year={2018},
  volume={19},
  pages={11:1-11:38}
}
Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorize-minimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating… 
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