GMRF-MM

Repository for our paper Effective Learning of a GMRF Mixture Model

Using the code

GMRF folder contains the single-gaussian estimators (including our implementation of GLASSO).
MixtureModel folder contains our EM-GMM implementation with an option to set the inverse-covariance estimator (set_Q_estimator).

Single Gaussian Example

DemoDebias.py recreates experiment A (of smaller dimension).

Mixture Model Example

DemoClustering.py recreates experiment B (of smaller dimension).

Copyright and License

This software is released under the MIT License (included with the software). Note, however, that if you are using this code (and/or the results of running it) to support any form of publication (e.g., a book, a journal paper, a conference paper, a patent application, etc.) then we request you will cite our paper:

@article{finder2022effective,
  title={Effective Learning of a GMRF Mixture Model},
  author={Finder, Shahaf E and Treister, Eran and Freifeld, Oren},
  journal={IEEE Access},
  year={2022},
  publisher={IEEE}
}