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 consistency in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to several popular competitors, to study its behaviour in finite samples. Biological validation of the algorithm is presented through the analysis of non-small cell lung cancer data.

Thi Kim Hue Nguyen, Monica Chiogna, Davide Risso, Erika Banzato (In stampa/Attività in corso). Guided structure learning of DAGs for count data. STATISTICAL MODELLING, To be defined, 1-25 [10.1177/1471082X241266738].

Guided structure learning of DAGs for count data

Monica Chiogna
Co-primo
;
In corso di stampa

Abstract

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 consistency in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to several popular competitors, to study its behaviour in finite samples. Biological validation of the algorithm is presented through the analysis of non-small cell lung cancer data.
In corso di stampa
Thi Kim Hue Nguyen, Monica Chiogna, Davide Risso, Erika Banzato (In stampa/Attività in corso). Guided structure learning of DAGs for count data. STATISTICAL MODELLING, To be defined, 1-25 [10.1177/1471082X241266738].
Thi Kim Hue Nguyen; Monica Chiogna; Davide Risso; Erika Banzato
File in questo prodotto:
File Dimensione Formato  
Guided_structure_learning_of_DAGs_for_count_data.pdf

accesso aperto

Descrizione: AAM
Tipo: Postprint
Licenza: Licenza per Accesso Aperto. Altra tipologia di licenza compatibile con Open Access
Dimensione 588.89 kB
Formato Adobe PDF
588.89 kB Adobe PDF Visualizza/Apri
nguyen-et-al-2024-guided-structure-learning-of-dags-for-count-data.pdf

accesso aperto

Descrizione: online first
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 317.48 kB
Formato Adobe PDF
317.48 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/973956
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact