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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2018
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| Materias: | |
| 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 |