Mostrar el registro sencillo del ítem
dc.contributor | 31249 | es_ES |
dc.contributor.other | https://orcid.org/0000-0002-7337-8974 | |
dc.coverage.spatial | Global | es_ES |
dc.creator | De la Rosa Vargas, José Ismael | |
dc.creator | Fleury, Gilles | |
dc.creator | Osuna, Sonia Esther | |
dc.creator | Davoust, Marie Eve | |
dc.date.accessioned | 2020-04-14T20:09:09Z | |
dc.date.available | 2020-04-14T20:09:09Z | |
dc.date.issued | 2006-02 | |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.issn | 0018- 9456 | es_ES |
dc.identifier.issn | 1557-9662 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1655 | |
dc.identifier.uri | https://doi.org/10.48779/90q5-gx28 | |
dc.description.abstract | The 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.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation | DOI: 10.1109/TIM.2005.861495 | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.source | IEEE Transaction on Instrumentation and Measurement, Vol. 55, No. 1, febrero 2006, pp. 112-122 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Gibbs sampling | es_ES |
dc.subject.other | indirect measurement | es_ES |
dc.subject.other | Markov chain Monte Carlo (MCMC) | es_ES |
dc.subject.other | Metropolis–Hastings (M–H) | es_ES |
dc.title | Markov Chain Monte Carlo Posterior Density Approximation for a Groove Dimensioning Purpose | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
El ítem tiene asociados los siguientes ficheros de licencia: