The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Influence of partially known parameter on flaw characterization in Eddy Current Testing by using a random walk MCMC method based on metamodeling
Open accessAbstract
First, we present the implementation of a random walk Metropolis-within-Gibbs (MWG) sampling method in flaw characterization based on a metamodeling method. The role of metamodeling is to reduce the computational time cost in Eddy Current Testing (ECT) forward model calculation. In such a way, the use of Markov Chain Monte Carlo (MCMC) methods becomes possible. Secondly, we analyze the influence of partially known parameters in Bayesian estimation. The objective is to evaluate the importance of providing more specific prior information. Simulation results show that even partially known information has great interest in providing more accurate flaw parameter estimations. The improvement ratio depends on the parameter dependence and the interest shows only when the provided information is specific enough.
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
(2006) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/978-0-387-40065-5 · Cited by 9,008
Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,452
Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond
Williams, C. K. I. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_23 · Cited by 659
Adaptive proposal distribution for random walk Metropolis algorithm
Haario, H., Saksman, E., & Tamminen, J. (1999) · Computational Statistics · doi:10.1007/s001800050022 · Cited by 417
Optimal experimental design and some related control problems
Pronzato, L. (2008) · Automatica · doi:10.1016/j.automatica.2007.05.016 · Cited by 243
Research works printed but not linked: 7
- 10.1063/1.349615 · doi:10.1063/1.349615
- 10.1063/1.356511 · doi:10.1063/1.356511
- 10.1109/TMAG.2006.872490 · doi:10.1109/tmag.2006.872490
- 10.1016/j.csda.2006.01.009 · doi:10.1016/j.csda.2006.01.009
- Sykulski J K, 2008, IET 7th International Conference on Computation in Electromagnetics, 44
- Kleijnen J P C, 2004, The J. the Operational Res. Society, 15, 303
- 10.1109/TMAG.2012.2236102 · doi:10.1109/tmag.2012.2236102
Cited by
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Merino, N., Aronson, H. S., Bojanova, D. P., Feyhl-Buska, J., Wong, M. L., Zhang, S., & Giovannelli, D. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00780 · Cited by 658
Everything You Wish to Know About Memristors But Are Afraid to Ask
Chua, L. (2015) · Radioengineering · doi:10.13164/re.2015.0319 · Cited by 481
Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-021-01910-w · Cited by 441
Fitria, T. N. (2023) · ELT Forum: Journal of English Language Teaching · doi:10.15294/elt.v12i1.64069 · Cited by 283
Harnessing AI for education 4.0: Drivers of personalized learning.
Barrera Castro, G. P., Chiappe, A., Becerra Rodriguez, D. F., & Sepulveda, F. G. (2024) · Electronic Journal of e-Learning · doi:10.34190/ejel.22.5.3467 · Cited by 132
Metadata
- Authors
- Cai, C., Rodet, T., & Lambert, M.
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2014-10-21
- Volume
- 542
- Pages
- 012009
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W2033815440
- Cited by
- 12,185
- References
- 14
- FWCI
- 85.43
- Type
- conference paper
- Language
- en
- Open access
- diamond
- Licence
- iopscience.iop.org/info/page/text-and-data-mining (tdm)
- creativecommons.org/licenses/by/3.0/ (vor)
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- Cai, C., Rodet, T., & Lambert, M. (2014). Influence of partially known parameter on flaw characterization in Eddy Current Testing by using a random walk MCMC method based on metamodeling. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/542/1/012009
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