Bayesian inference from measurement information
Autor(a) principal: | |
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Data de Publicação: | 1999 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do INMETRO |
Texto Completo: | http://hdl.handle.net/10926/1205 |
Resumo: | Bayesian inference about a quantity is made through the probability density function that describes the state of incomplete knowledge acquired from measurement. This approach can be applied advantageously to evaluate the data obtained from repeated measurements of a quantity, with or without added information on the variances or error bounds of the indicated values. Results are compared with those obtained using conventional statistical theory. It is concluded that Bayesian inference allows a flexible and natural characterization of the measurement uncertainty. |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBayesian inference from measurement information19992011-09-01T16:47:55Z2011-09-01T16:47:55ZBayesian inference about a quantity is made through the probability density function that describes the state of incomplete knowledge acquired from measurement. This approach can be applied advantageously to evaluate the data obtained from repeated measurements of a quantity, with or without added information on the variances or error bounds of the indicated values. Results are compared with those obtained using conventional statistical theory. It is concluded that Bayesian inference allows a flexible and natural characterization of the measurement uncertainty.8 p. : il.Submitted by Bruna Fernandes de Farias (bruna.fernandes_farias@yahoo.com.br) on 2011-08-31T13:47:38Z No. of bitstreams: 1 1999_Gregory.pdf: 110247 bytes, checksum: 44704602904571307cc22e645b6ab846 (MD5)Approved for entry into archive by Catarina Soares(cfsoares@inmetro.gov.br) on 2011-09-01T16:47:55Z (GMT) No. of bitstreams: 1 1999_Gregory.pdf: 110247 bytes, checksum: 44704602904571307cc22e645b6ab846 (MD5)Made available in DSpace on 2011-09-01T16:47:55Z (GMT). No. of bitstreams: 1 1999_Gregory.pdf: 110247 bytes, checksum: 44704602904571307cc22e645b6ab846 (MD5) Previous issue date: 1999enghttp://hdl.handle.net/10926/1205DMD_hdl_10926/1205LIRA, I.; KYRIAZIS, G. Bayesian inference from measurement information. Metrologia, v. 36, p. 163-169, 1999.Lira, I.Kyriazis, Gregory Amaralinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do INMETROinstname:Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO)instacron:INMETROLira_1999.pdfhttp://xrepo01s.inmetro.gov.br/bitstream/10926/1205/1/Lira_1999.pdfapplication/pdf110247http://xrepo01s.inmetro.gov.br/bitstream/10926/1205/1/Lira_1999.pdf44704602904571307cc22e645b6ab846MD510926_1205_1Lira_1999.pdf.txthttp://xrepo01s.inmetro.gov.br/bitstream/10926/1205/6/Lira_1999.pdf.txttext/plain31121http://xrepo01s.inmetro.gov.br/bitstream/10926/1205/6/Lira_1999.pdf.txtd8c5b57135b337b82f414781d7b4a94bMD510926_1205_62024-06-10T15:24:39Zoai:xrepo01s.inmetro.gov.br:10926/1205Repositório de Publicaçõeshttp://repositorios.inmetro.gov.br/oai/requestopendoar:2012-10-26T17:09:48Repositório Institucional do INMETRO - Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO)false |
dc.title.none.fl_str_mv |
Bayesian inference from measurement information |
title |
Bayesian inference from measurement information |
spellingShingle |
Bayesian inference from measurement information Lira, I. |
title_short |
Bayesian inference from measurement information |
title_full |
Bayesian inference from measurement information |
title_fullStr |
Bayesian inference from measurement information |
title_full_unstemmed |
Bayesian inference from measurement information |
title_sort |
Bayesian inference from measurement information |
author |
Lira, I. |
author_facet |
Lira, I. Kyriazis, Gregory Amaral |
author_role |
author |
author2 |
Kyriazis, Gregory Amaral |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Lira, I. Kyriazis, Gregory Amaral |
description |
Bayesian inference about a quantity is made through the probability density function that describes the state of incomplete knowledge acquired from measurement. This approach can be applied advantageously to evaluate the data obtained from repeated measurements of a quantity, with or without added information on the variances or error bounds of the indicated values. Results are compared with those obtained using conventional statistical theory. It is concluded that Bayesian inference allows a flexible and natural characterization of the measurement uncertainty. |
publishDate |
1999 |
dc.date.issued.fl_str_mv |
1999 |
dc.date.available.fl_str_mv |
2011-09-01T16:47:55Z |
dc.date.accessioned.fl_str_mv |
2011-09-01T16:47:55Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10926/1205 DMD_hdl_10926/1205 |
dc.identifier.citation.fl_str_mv |
LIRA, I.; KYRIAZIS, G. Bayesian inference from measurement information. Metrologia, v. 36, p. 163-169, 1999. |
url |
http://hdl.handle.net/10926/1205 |
identifier_str_mv |
DMD_hdl_10926/1205 LIRA, I.; KYRIAZIS, G. Bayesian inference from measurement information. Metrologia, v. 36, p. 163-169, 1999. |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.bitstream.fl_str_mv |
application/pdf text/plain |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do INMETRO instname:Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO) instacron:INMETRO |
instname_str |
Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO) |
instacron_str |
INMETRO |
institution |
INMETRO |
reponame_str |
Repositório Institucional do INMETRO |
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Repositório Institucional do INMETRO |
repository.name.fl_str_mv |
Repositório Institucional do INMETRO - Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO) |
repository.mail.fl_str_mv |
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1801499965590077440 |