Statistical inference in queueing networks with probing information

Detalhes bibliográficos
Autor(a) principal: Antunes, Nelson
Data de Publicação: 2022
Outros Autores: Jacinto, Gonçalo, Pacheco, António
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/18411
Resumo: Statistical inference of the model parameters of a queueing system from data is an essential part of the study of the system. The data collection procedure specifies the type of observations from the system which are available. The estimation of the parameters can follow standard statistical procedures if we can collect all the necessary information from the system (e.g. arrival times and service times of customers) [5]. This explains the initial limited literature in statistical inference of queueing systems compared with performance analysis studies. For a survey on estimation in queueing systems, see.
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spelling Statistical inference in queueing networks with probing informationStatistical inference of the model parameters of a queueing system from data is an essential part of the study of the system. The data collection procedure specifies the type of observations from the system which are available. The estimation of the parameters can follow standard statistical procedures if we can collect all the necessary information from the system (e.g. arrival times and service times of customers) [5]. This explains the initial limited literature in statistical inference of queueing systems compared with performance analysis studies. For a survey on estimation in queueing systems, see.SpringerSapientiaAntunes, NelsonJacinto, GonçaloPacheco, António2022-10-19T10:25:49Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/18411eng0257-013010.1007/s11134-022-09841-zinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:30:39Zoai:sapientia.ualg.pt:10400.1/18411Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:08:10.963721Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Statistical inference in queueing networks with probing information
title Statistical inference in queueing networks with probing information
spellingShingle Statistical inference in queueing networks with probing information
Antunes, Nelson
title_short Statistical inference in queueing networks with probing information
title_full Statistical inference in queueing networks with probing information
title_fullStr Statistical inference in queueing networks with probing information
title_full_unstemmed Statistical inference in queueing networks with probing information
title_sort Statistical inference in queueing networks with probing information
author Antunes, Nelson
author_facet Antunes, Nelson
Jacinto, Gonçalo
Pacheco, António
author_role author
author2 Jacinto, Gonçalo
Pacheco, António
author2_role author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Antunes, Nelson
Jacinto, Gonçalo
Pacheco, António
description Statistical inference of the model parameters of a queueing system from data is an essential part of the study of the system. The data collection procedure specifies the type of observations from the system which are available. The estimation of the parameters can follow standard statistical procedures if we can collect all the necessary information from the system (e.g. arrival times and service times of customers) [5]. This explains the initial limited literature in statistical inference of queueing systems compared with performance analysis studies. For a survey on estimation in queueing systems, see.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-19T10:25:49Z
2022
2022-01-01T00:00:00Z
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10.1007/s11134-022-09841-z
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