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Learning mixed graphical models with separate sparsity parameters and stability-based model selection
BACKGROUND: Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneou...
שמור ב:
| הוצא לאור ב: | BMC Bioinformatics |
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| Main Authors: | , , , |
| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
BioMed Central
2016
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| נושאים: | |
| גישה מקוונת: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4905606/ https://ncbi.nlm.nih.gov/pubmed/27294886 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-016-1039-0 |
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