Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1655
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dc.contributor31249es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7337-8974-
dc.coverage.spatialGlobales_ES
dc.creatorDe la Rosa Vargas, José Ismael-
dc.creatorFleury, Gilles-
dc.creatorOsuna, Sonia Esther-
dc.creatorDavoust, Marie Eve-
dc.date.accessioned2020-04-14T20:09:09Z-
dc.date.available2020-04-14T20:09:09Z-
dc.date.issued2006-02-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn0018- 9456es_ES
dc.identifier.issn1557-9662es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1655-
dc.identifier.urihttps://doi.org/10.48779/90q5-gx28-
dc.description.abstractThe purpose of this paper is to present a new approach for measurand uncertainty characterization. The Márkov chain Monte Carlo (MCMC) is applied to measurand probability density function (pdf) estimation, which is considered as an inverse problem. The measurement characterization is driven by the pdf estimation in a nonlinear Gaussian framework with unknown variance and with limited observed data. These techniques are applied to a realistic measurand problem of groove dimensioning using remote field eddy current (RFEC) inspection. The application of resampling methods such as bootstrap and the perfect sampling for convergence diagnostics purposes gives large improvements in the accuracy of the MCMC estimates.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relationDOI: 10.1109/TIM.2005.861495es_ES
dc.relation.urigeneralPublices_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourceIEEE Transaction on Instrumentation and Measurement, Vol. 55, No. 1, febrero 2006, pp. 112-122es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherGibbs samplinges_ES
dc.subject.otherindirect measurementes_ES
dc.subject.otherMarkov chain Monte Carlo (MCMC)es_ES
dc.subject.otherMetropolis–Hastings (M–H)es_ES
dc.titleMarkov Chain Monte Carlo Posterior Density Approximation for a Groove Dimensioning Purposees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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