Artículos de revistas
Markov Chain Monte Carlo Posterior Density Approximation for a Groove Dimensioning Purpose
Fecha
2006-02Registro en:
0018- 9456
1557-9662
Autor
De la Rosa Vargas, José Ismael
Fleury, Gilles
Osuna, Sonia Esther
Davoust, Marie Eve
Institución
Resumen
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.