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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Vydáno v:Int J Data Sci Anal
Hlavní autoři: Raghu, Vineet K., Ramsey, Joseph D., Morris, Alison, Manatakis, Dimitrios V., Sprites, Peter, Chrysanthis, Panos K., Glymour, Clark, Benos, Panayiotis V.
Médium: Artigo
Jazyk:Inglês
Vydáno: Springer International Publishing 2018
Témata:
Regular Paper
On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC6096780/
https://ncbi.nlm.nih.gov/pubmed/30148202
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s41060-018-0104-3
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