Absfmcf-Thepurposeof thispaper b topresent (I new approach for measurement uncertain@ chnmcteriiotion. The Markov Chain Monte Cor10 (MCMC) is applied to m e o t pdf estimation, which is considered as on inverseproblem. The meosuremenl ehsrmlerirntion is driven by the pdf estimation in (I non-linear Gaussian fromrwork wilh unknown varionee and with limited obserwd daln Multidimensionnl integrrrtion a.nd support seorehing, are driven by fhc Mdropolis-H.sfings (M-H) autoregressive 01-gorilhm which perfonnonce isgenerdly bener fhan the M-H random wlk These techniques nre applied to (I redistic meclsurementproblcnt of groow dimrnsioning using Remote Field Eddy Current (RFEC) inspection The applicntion of resampling methods such as boofstrop and the perfect snmp h g for convergence diagnosticspurposes, gives large improwments in fhc accuracy of Ihc M C M C rrtimnfer.
Abstract-The purpose of this paper is to present a new approach for measurand uncertainty characterization. The Markov 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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