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Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types

In recent years, great strides have been made for causal structure learning in the high-dimensional setting and in the mixed data-type setting when there are both discrete and continuous variables. However, due to the complications involved with modeling continuous-discrete variable interactions, th...

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Detalles Bibliográficos
Publicado en:Proc Mach Learn Res
Autores principales: Andrews, Bryan, Ramsey, Joseph, Cooper, Gregory F.
Formato: Artigo
Lenguaje:Inglês
Publicado: 2019
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Acceso en línea:https://ncbi.nlm.nih.gov/pmc/articles/PMC6709674/
https://ncbi.nlm.nih.gov/pubmed/31453569
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