Markov Chain Monte Carlo Posterior Density Approximation for a Groove Dimensioning Purpose
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 p...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2006 |
| País: | México |
| Recursos: | Universidad Autónoma de Zacatecas |
| Repositório: | Repositorio Institucional Caxcán |
| Idioma: | inglês |
| OAI Identifier: | oai:http://ricaxcan.uaz.edu.mx:20.500.11845/1655 |
| Acesso em linha: | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1655 https://doi.org/10.48779/90q5-gx28 |
| Access Level: | Acceso aberto |
| Palavra-chave: | INGENIERIA Y TECNOLOGIA [7] Gibbs sampling indirect measurement Markov chain Monte Carlo (MCMC) Metropolis–Hastings (M–H) |
| Resumo: | 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. |
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