Regression‐based Bayesian estimation and structure learning for nonparanormal graphical models

A nonparanormal graphical model is a semiparametric generalization of a Gaussian graphical model for continuous variables in which it is assumed that the variables follow a Gaussian graphical model only after some unknown smooth monotone transformations. We consider a Bayesian approach to inference...

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Detalles Bibliográficos
Autores principales: Mulgrave, Jami J., Ghosal, Subhashis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wiley Subscription Services, Inc., A Wiley Company 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455150/
https://www.ncbi.nlm.nih.gov/pubmed/36090618
http://dx.doi.org/10.1002/sam.11576

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455150/
https://www.ncbi.nlm.nih.gov/pubmed/36090618
http://dx.doi.org/10.1002/sam.11576

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