Sum-Product Networks: A Survey

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabil...

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Detalles Bibliográficos
Autores: Sánchez Cauce, Raquel, París Fernández, Iago, Díez Vegas, Francisco Javier
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12464
Acceso en línea:https://hdl.handle.net/20.500.14468/12464
Access Level:acceso abierto
Palabra clave:Sum-product networks
probabilistic graphical models
Bayesian networks
machine learning
deep neural networks
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spelling Sum-Product Networks: A SurveySánchez Cauce, RaquelParís Fernández, IagoDíez Vegas, Francisco JavierSum-product networksprobabilistic graphical modelsBayesian networksmachine learningdeep neural networksA sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.IEEEe-Spacio UNED20242024-05-2020212021-02-2520212021-02-25journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12464reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/124642026-06-06T12:38:31Z
dc.title.none.fl_str_mv Sum-Product Networks: A Survey
title Sum-Product Networks: A Survey
spellingShingle Sum-Product Networks: A Survey
Sánchez Cauce, Raquel
Sum-product networks
probabilistic graphical models
Bayesian networks
machine learning
deep neural networks
title_short Sum-Product Networks: A Survey
title_full Sum-Product Networks: A Survey
title_fullStr Sum-Product Networks: A Survey
title_full_unstemmed Sum-Product Networks: A Survey
title_sort Sum-Product Networks: A Survey
dc.creator.none.fl_str_mv Sánchez Cauce, Raquel
París Fernández, Iago
Díez Vegas, Francisco Javier
author Sánchez Cauce, Raquel
author_facet Sánchez Cauce, Raquel
París Fernández, Iago
Díez Vegas, Francisco Javier
author_role author
author2 París Fernández, Iago
Díez Vegas, Francisco Javier
author2_role author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Sum-product networks
probabilistic graphical models
Bayesian networks
machine learning
deep neural networks
topic Sum-product networks
probabilistic graphical models
Bayesian networks
machine learning
deep neural networks
description A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-02-25
2021
2021-02-25
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12464
url https://hdl.handle.net/20.500.14468/12464
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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