Structural learning of simple staged trees
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. How...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | IE |
| Repositorio: | Repositorio IE |
| OAI Identifier: | oai:repositorio.ie.edu:20.500.14417/3916 |
| Acceso en línea: | https://doi.org/10.1007/s10618-024-01007-0 https://hdl.handle.net/20.500.14417/3916 https://link.springer.com/article/10.1007/s10618-024-01007-0 |
| Access Level: | acceso abierto |
| Palabra clave: | Asymmetric graphical models Bayesian networks Context-specific independence Staged trees Structural learning 33 Ciencias Tecnológicas ODS 9 - Industria, innovación e infraestructura |
| Sumario: | Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here, we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences. |
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