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- W2060509720 startingPage "2680" @default.
- W2060509720 abstract "Regression analysis that incorporates measurement errors in input variables is important in various applications. In this study, we consider this problem within a framework of Gaussian process regression. The proposed method can also be regarded as a generalization of kernel regression to include errors in regressors. A Markov chain Monte Carlo method is introduced, where the infinite-dimensionality of Gaussian process is dealt with a trick to exchange the order of sampling of the latent variable and the function. The proposed method is tested with artificial data." @default.
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- W2060509720 date "2010-01-01" @default.
- W2060509720 modified "2023-10-15" @default.
- W2060509720 title "Gaussian Process Regression with Measurement Error" @default.
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- W2060509720 doi "https://doi.org/10.1587/transinf.e93.d.2680" @default.
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