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Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints
Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and ou...
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| Pubblicato in: | Mach Learn Knowl Discov Databases |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2017
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5836552/ https://ncbi.nlm.nih.gov/pubmed/29520396 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-3-319-71246-8_9 |
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