Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear

Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they can only formally encode symmetric conditional independence, which is often too strict to hold in practice. Asymmetry-labeled DAGs have been recently proposed to extend the class...

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Detalles Bibliográficos
Autores: Leonelli, Manuele, Varando, Gherardo
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/3917
Acceso en línea:https://doi.org/10.1007/s10489-024-05268-6
https://hdl.handle.net/20.500.14417/3917
https://link.springer.com/article/10.1007/s10489-024-05268-6
Access Level:acceso abierto
Palabra clave:Bayesian networks
Conditional independence
Probabilistic graphical models
Staged trees
Structural learning
33 Ciencias Tecnológicas
ODS 3 - Salud y bienestar
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spelling Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fearLeonelli, ManueleVarando, GherardoBayesian networksConditional independenceProbabilistic graphical modelsStaged treesStructural learning33 Ciencias TecnológicasODS 3 - Salud y bienestarBayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they can only formally encode symmetric conditional independence, which is often too strict to hold in practice. Asymmetry-labeled DAGs have been recently proposed to extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denoting the dependence between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models, which, whilst efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.yesPublishedSpringer Naturehttps://ror.org/02jjdwm7520252024info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1007/s10489-024-05268-6https://hdl.handle.net/20.500.14417/3917https://link.springer.com/article/10.1007/s10489-024-05268-6reponame:Repositorio IEinstname:IEInglésIE School of Science & TechnologyIE UniversityApplied MathematicsAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/deedinfo:eu-repo/semantics/openAccessoai:repositorio.ie.edu:20.500.14417/39172026-06-15T12:40:57Z
dc.title.none.fl_str_mv Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
title Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
spellingShingle Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
Leonelli, Manuele
Bayesian networks
Conditional independence
Probabilistic graphical models
Staged trees
Structural learning
33 Ciencias Tecnológicas
ODS 3 - Salud y bienestar
title_short Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
title_full Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
title_fullStr Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
title_full_unstemmed Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
title_sort Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
dc.creator.none.fl_str_mv Leonelli, Manuele
Varando, Gherardo
author Leonelli, Manuele
author_facet Leonelli, Manuele
Varando, Gherardo
author_role author
author2 Varando, Gherardo
author2_role author
dc.contributor.none.fl_str_mv https://ror.org/02jjdwm75
dc.subject.none.fl_str_mv Bayesian networks
Conditional independence
Probabilistic graphical models
Staged trees
Structural learning
33 Ciencias Tecnológicas
ODS 3 - Salud y bienestar
topic Bayesian networks
Conditional independence
Probabilistic graphical models
Staged trees
Structural learning
33 Ciencias Tecnológicas
ODS 3 - Salud y bienestar
description Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they can only formally encode symmetric conditional independence, which is often too strict to hold in practice. Asymmetry-labeled DAGs have been recently proposed to extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denoting the dependence between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models, which, whilst efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s10489-024-05268-6
https://hdl.handle.net/20.500.14417/3917
https://link.springer.com/article/10.1007/s10489-024-05268-6
url https://doi.org/10.1007/s10489-024-05268-6
https://hdl.handle.net/20.500.14417/3917
https://link.springer.com/article/10.1007/s10489-024-05268-6
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IE School of Science & Technology
IE University
Applied Mathematics
dc.rights.none.fl_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/deed
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/deed
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:Repositorio IE
instname:IE
instname_str IE
reponame_str Repositorio IE
collection Repositorio IE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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