Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data (“Mixed Data”), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inf...

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Detalles Bibliográficos
Autores principales: Raghu, Vineet K., Ramsey, Joseph D., Morris, Alison, Manatakis, Dimitrios V., Sprites, Peter, Chrysanthis, Panos K., Glymour, Clark, Benos, Panayiotis V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Regular Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096780/
https://www.ncbi.nlm.nih.gov/pubmed/30148202
http://dx.doi.org/10.1007/s41060-018-0104-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096780/
https://www.ncbi.nlm.nih.gov/pubmed/30148202
http://dx.doi.org/10.1007/s41060-018-0104-3

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