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...

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Detalles Bibliográficos
Autores principales: Sedgewick, Andrew J., Shi, Ivy, Donovan, Rory M., Benos, Panayiotis V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905606/
https://www.ncbi.nlm.nih.gov/pubmed/27294886
http://dx.doi.org/10.1186/s12859-016-1039-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905606/
https://www.ncbi.nlm.nih.gov/pubmed/27294886
http://dx.doi.org/10.1186/s12859-016-1039-0

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