Robust learning of staged tree models: A case study in evaluating transport services

Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been implemented in various pieces of software. However, t...

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Detalhes bibliográficos
Autores: Leonelli, Manuele, Varando, Gherardo
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:IE
Repositorio:Repositorio IE
OAI Identifier:oai:repositorio.ie.edu:20.500.14417/3915
Acesso em linha:https://doi.org/10.1016/j.seps.2024.102030
https://hdl.handle.net/20.500.14417/3915
https://www.sciencedirect.com/science/article/pii/S0038012124002295
Access Level:acceso abierto
Palavra-chave:Bayesian networks
Conditional independence
Service evaluation
Staged trees
What-if analysis
33 Ciencias Tecnológicas
ODS 11 - Ciudades y comunidades sostenibles
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oai_identifier_str oai:repositorio.ie.edu:20.500.14417/3915
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spelling Robust learning of staged tree models: A case study in evaluating transport servicesLeonelli, ManueleVarando, GherardoBayesian networksConditional independenceService evaluationStaged treesWhat-if analysis33 Ciencias TecnológicasODS 11 - Ciudades y comunidades sosteniblesStaged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been implemented in various pieces of software. However, to date, methods to assess the robustness and validity of the learned, non-symmetric relationships are not available. Here, we introduce validation techniques tailored to staged tree models based on non-parametric bootstrap resampling methods and investigate their use in practical applications. In particular, we focus on the evaluation of transport services using large-scale survey data. In these types of applications, data from heterogeneous sources must be collated together. Staged trees provide a natural framework for this integration of data and its analysis. For the thorough evaluation of transport services, we further implement novel what-if sensitivity analyses for staged trees and their visualization using software.yesPublishedElsevierhttps://ror.org/02jjdwm7520252024info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1016/j.seps.2024.102030https://hdl.handle.net/20.500.14417/3915https://www.sciencedirect.com/science/article/pii/S0038012124002295reponame: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:20.500.14417/39152026-06-15T12:40:57Z
dc.title.none.fl_str_mv Robust learning of staged tree models: A case study in evaluating transport services
title Robust learning of staged tree models: A case study in evaluating transport services
spellingShingle Robust learning of staged tree models: A case study in evaluating transport services
Leonelli, Manuele
Bayesian networks
Conditional independence
Service evaluation
Staged trees
What-if analysis
33 Ciencias Tecnológicas
ODS 11 - Ciudades y comunidades sostenibles
title_short Robust learning of staged tree models: A case study in evaluating transport services
title_full Robust learning of staged tree models: A case study in evaluating transport services
title_fullStr Robust learning of staged tree models: A case study in evaluating transport services
title_full_unstemmed Robust learning of staged tree models: A case study in evaluating transport services
title_sort Robust learning of staged tree models: A case study in evaluating transport services
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
Service evaluation
Staged trees
What-if analysis
33 Ciencias Tecnológicas
ODS 11 - Ciudades y comunidades sostenibles
topic Bayesian networks
Conditional independence
Service evaluation
Staged trees
What-if analysis
33 Ciencias Tecnológicas
ODS 11 - Ciudades y comunidades sostenibles
description Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been implemented in various pieces of software. However, to date, methods to assess the robustness and validity of the learned, non-symmetric relationships are not available. Here, we introduce validation techniques tailored to staged tree models based on non-parametric bootstrap resampling methods and investigate their use in practical applications. In particular, we focus on the evaluation of transport services using large-scale survey data. In these types of applications, data from heterogeneous sources must be collated together. Staged trees provide a natural framework for this integration of data and its analysis. For the thorough evaluation of transport services, we further implement novel what-if sensitivity analyses for staged trees and their visualization using software.
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/10.1016/j.seps.2024.102030
https://hdl.handle.net/20.500.14417/3915
https://www.sciencedirect.com/science/article/pii/S0038012124002295
url https://doi.org/10.1016/j.seps.2024.102030
https://hdl.handle.net/20.500.14417/3915
https://www.sciencedirect.com/science/article/pii/S0038012124002295
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 Elsevier
publisher.none.fl_str_mv Elsevier
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 15,81155

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