LR-GLM: High-dimensional Bayesian inference using low-rank data approximations

LR-GLM: High-dimensional Bayesian inference using low-rank data approximations

Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, t...

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Detalles Bibliográficos
Main Authors: Trippe, Brian L., Agrawal, Raj, Broderick, Tamara A
Outros autores: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Formato: Artigo
Idioma:English
Publicado: MIT Press 2020
Acceso en liña:https://hdl.handle.net/1721.1/128775

Internet

https://hdl.handle.net/1721.1/128775

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