Recursive variance reduction in reliability analysis

Canale, Eduardo - Cancela, Héctor - Romero, Pablo - Robledo Amoza, Franco Rafael

Resumen:

Network reliability deals with reliability metrics of large classes of mul- ticomponent systems. Recursive Variance Reduction (RVR) is a powerful pointwise estimation method, widely applied in network reliability anal- ysis. In this paper, RVR is extended to arbitrary Stochastic Binary Sys- tems, with minor requirements. Additionally, its variance is again lower than Crude Monte Carlo (CMC), in this general context.


Detalles Bibliográficos
2014
Stochastic binary system
Network reliability
Recursive variance reduction
Crude Monte Carlo
Inglés
Universidad de la República
COLIBRI
http://hdl.handle.net/20.500.12008/5170
Acceso abierto
Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0)
_version_ 1807522945371734016
author Canale, Eduardo
author2 Cancela, Héctor
Romero, Pablo
Robledo Amoza, Franco Rafael
author2_role author
author
author
author_facet Canale, Eduardo
Cancela, Héctor
Romero, Pablo
Robledo Amoza, Franco Rafael
author_role author
bitstream.checksum.fl_str_mv 6429389a7df7277b72b7924fdc7d47a9
4afdbb8c545fd630ea7db775da747b2f
ae2fe251842ade1134c5d9bb99b6eefe
d41d8cd98f00b204e9800998ecf8427e
ca7e404c8e908287d86164deb80c5254
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
bitstream.url.fl_str_mv http://localhost:8080/xmlui/bitstream/20.500.12008/5170/5/license.txt
http://localhost:8080/xmlui/bitstream/20.500.12008/5170/2/license_url
http://localhost:8080/xmlui/bitstream/20.500.12008/5170/3/license_text
http://localhost:8080/xmlui/bitstream/20.500.12008/5170/4/license_rdf
http://localhost:8080/xmlui/bitstream/20.500.12008/5170/1/TR1415.pdf
collection COLIBRI
dc.contributor.filiacion.es.fl_str_mv Canale Eduardo, Universidad de la República (Uruguay). Facultad de Ingenieria.
Cancela Héctor, Universidad de la República (Uruguay). Facultad de Ingenieria.
Romero Pablo, Universidad de la República (Uruguay). Facultad de Ingenieria.
Robledo Franco, Universidad de la República (Uruguay). Facultad de Ingenieria.
dc.creator.none.fl_str_mv Canale, Eduardo
Cancela, Héctor
Romero, Pablo
Robledo Amoza, Franco Rafael
dc.date.accessioned.none.fl_str_mv 2015-12-14T12:52:52Z
dc.date.available.none.fl_str_mv 2015-12-14T12:52:52Z
dc.date.issued.none.fl_str_mv 2014
dc.description.abstract.none.fl_txt_mv Network reliability deals with reliability metrics of large classes of mul- ticomponent systems. Recursive Variance Reduction (RVR) is a powerful pointwise estimation method, widely applied in network reliability anal- ysis. In this paper, RVR is extended to arbitrary Stochastic Binary Sys- tems, with minor requirements. Additionally, its variance is again lower than Crude Monte Carlo (CMC), in this general context.
dc.format.extent.es.fl_str_mv 11 p.
dc.format.mimetype.en.fl_str_mv aplication/pdf
dc.identifier.citation.es.fl_str_mv CANALE, E., CANCELA, H., ROMERO, P., y otros. "Recursive variance reduction in reliability analysis". Montevideo : UR.FI-INCO, 2014. Reportes Técnicos 14-15.
dc.identifier.issn.none.fl_str_mv 0797-6410
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12008/5170
dc.language.iso.none.fl_str_mv en
eng
dc.publisher.es.fl_str_mv UR.FI-INCO
dc.relation.ispartof.none.fl_str_mv Reportes Técnicos 14-15
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
dc.subject.en.fl_str_mv Stochastic binary system
Network reliability
Recursive variance reduction
Crude Monte Carlo
dc.title.none.fl_str_mv Recursive variance reduction in reliability analysis
dc.type.es.fl_str_mv Reporte técnico
dc.type.none.fl_str_mv info:eu-repo/semantics/report
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
description Network reliability deals with reliability metrics of large classes of mul- ticomponent systems. Recursive Variance Reduction (RVR) is a powerful pointwise estimation method, widely applied in network reliability anal- ysis. In this paper, RVR is extended to arbitrary Stochastic Binary Sys- tems, with minor requirements. Additionally, its variance is again lower than Crude Monte Carlo (CMC), in this general context.
eu_rights_str_mv openAccess
format report
id COLIBRI_b2195669686296b62ddf2080906822bb
identifier_str_mv CANALE, E., CANCELA, H., ROMERO, P., y otros. "Recursive variance reduction in reliability analysis". Montevideo : UR.FI-INCO, 2014. Reportes Técnicos 14-15.
0797-6410
instacron_str Universidad de la República
institution Universidad de la República
instname_str Universidad de la República
language eng
language_invalid_str_mv en
network_acronym_str COLIBRI
network_name_str COLIBRI
oai_identifier_str oai:colibri.udelar.edu.uy:20.500.12008/5170
publishDate 2014
reponame_str COLIBRI
repository.mail.fl_str_mv mabel.seroubian@seciu.edu.uy
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository_id_str 4771
rights_invalid_str_mv Licencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0)
spelling Canale Eduardo, Universidad de la República (Uruguay). Facultad de Ingenieria.Cancela Héctor, Universidad de la República (Uruguay). Facultad de Ingenieria.Romero Pablo, Universidad de la República (Uruguay). Facultad de Ingenieria.Robledo Franco, Universidad de la República (Uruguay). Facultad de Ingenieria.2015-12-14T12:52:52Z2015-12-14T12:52:52Z2014CANALE, E., CANCELA, H., ROMERO, P., y otros. "Recursive variance reduction in reliability analysis". Montevideo : UR.FI-INCO, 2014. Reportes Técnicos 14-15.0797-6410http://hdl.handle.net/20.500.12008/5170Network reliability deals with reliability metrics of large classes of mul- ticomponent systems. Recursive Variance Reduction (RVR) is a powerful pointwise estimation method, widely applied in network reliability anal- ysis. In this paper, RVR is extended to arbitrary Stochastic Binary Sys- tems, with minor requirements. Additionally, its variance is again lower than Crude Monte Carlo (CMC), in this general context.Submitted by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2015-12-14T12:52:52Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) TR1415.pdf: 161750 bytes, checksum: ca7e404c8e908287d86164deb80c5254 (MD5)Made available in DSpace on 2015-12-14T12:52:52Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) TR1415.pdf: 161750 bytes, checksum: ca7e404c8e908287d86164deb80c5254 (MD5) Previous issue date: 201411 p.aplication/pdfenengUR.FI-INCOReportes Técnicos 14-15Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución – No Comercial – Sin Derivadas (CC BY-NC-ND 4.0)Stochastic binary systemNetwork reliabilityRecursive variance reductionCrude Monte CarloRecursive variance reduction in reliability analysisReporte técnicoinfo:eu-repo/semantics/reportinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaCanale, EduardoCancela, HéctorRomero, PabloRobledo Amoza, Franco RafaelLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/5170/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://localhost:8080/xmlui/bitstream/20.500.12008/5170/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-821468http://localhost:8080/xmlui/bitstream/20.500.12008/5170/3/license_textae2fe251842ade1134c5d9bb99b6eefeMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://localhost:8080/xmlui/bitstream/20.500.12008/5170/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54ORIGINALTR1415.pdfTR1415.pdfapplication/pdf161750http://localhost:8080/xmlui/bitstream/20.500.12008/5170/1/TR1415.pdfca7e404c8e908287d86164deb80c5254MD5120.500.12008/51702016-06-16 15:10:06.762oai:colibri.udelar.edu.uy:20.500.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Universidadhttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestmabel.seroubian@seciu.edu.uyUruguayopendoar:47712024-07-25T14:34:02.342443COLIBRI - Universidad de la Repúblicafalse
spellingShingle Recursive variance reduction in reliability analysis
Canale, Eduardo
Stochastic binary system
Network reliability
Recursive variance reduction
Crude Monte Carlo
status_str publishedVersion
title Recursive variance reduction in reliability analysis
title_full Recursive variance reduction in reliability analysis
title_fullStr Recursive variance reduction in reliability analysis
title_full_unstemmed Recursive variance reduction in reliability analysis
title_short Recursive variance reduction in reliability analysis
title_sort Recursive variance reduction in reliability analysis
topic Stochastic binary system
Network reliability
Recursive variance reduction
Crude Monte Carlo
url http://hdl.handle.net/20.500.12008/5170