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View Code? Open in Web Editor NEWEvaluating dependencies among random variables.
Home Page: https://depynd.readthedocs.io
License: MIT License
Evaluating dependencies among random variables.
Home Page: https://depynd.readthedocs.io
License: MIT License
module mutual_information
-> information
Liu, Han, Kathryn Roeder, and Larry Wasserman. "Stability approach to regularization selection (stars) for high dimensional graphical models." Advances in neural information processing systems. 2010.
https://arxiv.org/pdf/1006.3316.pdf
Zhao, Peng, and Bin Yu. "On model selection consistency of Lasso." Journal of Machine learning research 7.Nov (2006): 2541-2563.
http://www.jmlr.org/papers/volume7/zhao06a/zhao06a.pdf
The method depynd.bayesian_networks.mmhc()
is implemented using mutual information scoring function, which has no theoretical guarantee to work on discrete-continuous mixtures.
After #7.
Foygel, Rina, and Mathias Drton. "Extended Bayesian information criteria for Gaussian graphical models." Advances in neural information processing systems. 2010.
https://papers.nips.cc/paper/4087-extended-bayesian-information-criteria-for-gaussian-graphical-models.pdf
The current implementation of the structure learning of bayesian networks has no theoretical guarantee.
Lee, Jason, and Trevor Hastie. "Structure learning of mixed graphical models." Artificial Intelligence and Statistics. 2013.
http://www.jmlr.org/proceedings/papers/v31/lee13a.pdf
The 'graph_lasso' was renamed to 'graphical_lasso' in version 0.20 and will be removed in 0.22.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/graph_lasso_.py#L715
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