Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models

@article{Meng2014MarginalLF,
  title={Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models},
  author={Zhaoshi Meng and Dennis L. Wei and A. Wiesel and A. Hero},
  journal={IEEE Transactions on Signal Processing},
  year={2014},
  volume={62},
  pages={5425-5438}
}
We consider distributed estimation of the inverse covariance matrix, also called the concentration or precision matrix, in Gaussian graphical models. Traditional centralized estimation often requires global inference of the covariance matrix, which can be computationally intensive in large dimensions. Approximate inference based on message-passing algorithms, on the other hand, can lead to unstable and biased estimation in loopy graphical models. Here, we propose a general framework for… Expand
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