期刊
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
卷 E93D, 期 10, 页码 2680-2689出版社
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
DOI: 10.1587/transinf.E93.D.2680
关键词
measurement error; errors in input variables; kernel; Gaussian process; Bayes; Markov chain Monte Carlo
资金
- KAKENHI [18500184]
- Grants-in-Aid for Scientific Research [18500184] Funding Source: KAKEN
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.
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