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  "Title": "Gaussian Mixture Graphical Model Learning and Inference",
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  "Description": "Gaussian mixture graphical models include Bayesian\nnetworks and dynamic Bayesian networks (their temporal\nextension) whose local probability distributions are described\nby Gaussian mixture models. They are powerful tools for\ngraphically and quantitatively representing nonlinear\ndependencies between continuous variables. This package\nprovides a complete framework to create, manipulate, learn the\nstructure and the parameters, and perform inference in these\nmodels. Most of the algorithms are described in the PhD thesis\nof Roos (2018) <https://theses.hal.science/tel-01943718>.",
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