Learning Sets of Bayesian Networks

This paper considers the problem of learning a generalized credal network (a set of Bayesian networks) from a dataset. It is based on using the BDEu score and computes all the networks with score above a predetermined factor of the optimal one. To avoid the problem of determining the equivalent samp...

Descripción completa

Detalles Bibliográficos
Autores principales: Cano, Andrés, Gómez-Olmedo, Manuel, Moral, Serafín
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274759/
http://dx.doi.org/10.1007/978-3-030-50143-3_12
_version_ 1783542654754095104
author Cano, Andrés
Gómez-Olmedo, Manuel
Moral, Serafín
author_facet Cano, Andrés
Gómez-Olmedo, Manuel
Moral, Serafín
author_sort Cano, Andrés
collection PubMed
description This paper considers the problem of learning a generalized credal network (a set of Bayesian networks) from a dataset. It is based on using the BDEu score and computes all the networks with score above a predetermined factor of the optimal one. To avoid the problem of determining the equivalent sample size (ESS), the approach also considers the possibility of an undetermined ESS. Even if the final result is a set of Bayesian networks, the paper also studies the problem of selecting a single network with some alternative procedures. Finally, some preliminary experiments are carried out with three small networks.
format Online
Article
Text
id pubmed-7274759
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-72747592020-06-08 Learning Sets of Bayesian Networks Cano, Andrés Gómez-Olmedo, Manuel Moral, Serafín Information Processing and Management of Uncertainty in Knowledge-Based Systems Article This paper considers the problem of learning a generalized credal network (a set of Bayesian networks) from a dataset. It is based on using the BDEu score and computes all the networks with score above a predetermined factor of the optimal one. To avoid the problem of determining the equivalent sample size (ESS), the approach also considers the possibility of an undetermined ESS. Even if the final result is a set of Bayesian networks, the paper also studies the problem of selecting a single network with some alternative procedures. Finally, some preliminary experiments are carried out with three small networks. 2020-05-15 /pmc/articles/PMC7274759/ http://dx.doi.org/10.1007/978-3-030-50143-3_12 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Cano, Andrés
Gómez-Olmedo, Manuel
Moral, Serafín
Learning Sets of Bayesian Networks
title Learning Sets of Bayesian Networks
title_full Learning Sets of Bayesian Networks
title_fullStr Learning Sets of Bayesian Networks
title_full_unstemmed Learning Sets of Bayesian Networks
title_short Learning Sets of Bayesian Networks
title_sort learning sets of bayesian networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274759/
http://dx.doi.org/10.1007/978-3-030-50143-3_12
work_keys_str_mv AT canoandres learningsetsofbayesiannetworks
AT gomezolmedomanuel learningsetsofbayesiannetworks
AT moralserafin learningsetsofbayesiannetworks

Ejemplares similares