Efficient model choice and parameter estimation by using Nested Sampling applied in Eddy-Current Testing

Abstract : In many applications, such as Eddy-Current Testing (ECT), we are often interested in the joint model choice and parameter estimation. Nested Sampling (NS) is one of the possible methods. The key step that reflects the efficiency of the NS algorithm is how to get samples with hard constraint on the likelihood value. This contribution is based on the classical idea where the new sample is drawn within a hyper-ellipsoid, the latter being located from Gaussian approximation. This sampling strategy can automatically guarantee the hard constraint on the likelihood. Meanwhile, it shows the best sampling efficiency for models which have Gaussian-like likelihood distributions. We apply this method in ECT. The simulation results show that this method has high model choice ability and good parameter estimation accuracy, and low computational cost meanwhile.
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Communication dans un congrès
IEEE. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), Apr 2015, Brisbane, Australia. pp.4165-4169, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 〈10.1109/ICASSP.2015.7178755〉
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Dernière modification le : jeudi 13 septembre 2018 - 15:24:04
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Caifang Cai, Thomas Rodet, Marc Lambert. Efficient model choice and parameter estimation by using Nested Sampling applied in Eddy-Current Testing. IEEE. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), Apr 2015, Brisbane, Australia. pp.4165-4169, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 〈10.1109/ICASSP.2015.7178755〉. 〈hal-01207386〉

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