Causal structure learning in directed, possibly cyclic, graphical models
We consider the problem of learning a directed graph G⋆{G}^{\star } from observational data. We assume that the distribution that gives rise to the samples is Markov and faithful to the graph G⋆{G}^{\star } and that there are no unobserved variables. We do not rely on any further assumptions regardi...
Sparad:
| Huvudupphov: | , |
|---|---|
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
De Gruyter
2025-04-01
|
| Serie: | Journal of Causal Inference |
| Ämnen: | |
| Länkar: | https://doi.org/10.1515/jci-2024-0037 |
| Taggar: |
Inga taggar, Lägg till första taggen!
|