Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2206.09754 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916095587254272 |
|---|---|
| author | Nguyen, Thi Kim Hue Chiogna, Monica Risso, Davide Banzato, Erika |
| author_facet | Nguyen, Thi Kim Hue Chiogna, Monica Risso, Davide Banzato, Erika |
| contents | In this paper, we tackle structure learning of Directed Acyclic Graphs (DAGs), with the idea of exploiting available prior knowledge of the domain at hand to guide the search of the best structure. In particular, we assume to know the topological ordering of variables in addition to the given data. We study a new algorithm for learning the structure of DAGs, proving its theoretical consistence in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to a number of popular competitors, in order to study its behavior in finite samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_09754 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Guided structure learning of DAGs for count data Nguyen, Thi Kim Hue Chiogna, Monica Risso, Davide Banzato, Erika Methodology In this paper, we tackle structure learning of Directed Acyclic Graphs (DAGs), with the idea of exploiting available prior knowledge of the domain at hand to guide the search of the best structure. In particular, we assume to know the topological ordering of variables in addition to the given data. We study a new algorithm for learning the structure of DAGs, proving its theoretical consistence in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to a number of popular competitors, in order to study its behavior in finite samples. |
| title | Guided structure learning of DAGs for count data |
| topic | Methodology |
| url | https://arxiv.org/abs/2206.09754 |