IEICE TRANSACTIONS on Information
Gaussian Process Regression with Measurement Error
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.10 pp.2680-2689
- Publication Date
- 2010/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.2680
- Type of Manuscript
- Special Section PAPER (Special Section on Data Mining and Statistical Science)
- Category
Authors
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Yukito IBA, Shotaro AKAHO, "Gaussian Process Regression with Measurement Error" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2680-2689, October 2010, doi: 10.1587/transinf.E93.D.2680.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2680/_p
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@ARTICLE{e93-d_10_2680,
author={Yukito IBA, Shotaro AKAHO, },
journal={IEICE TRANSACTIONS on Information},
title={Gaussian Process Regression with Measurement Error},
year={2010},
volume={E93-D},
number={10},
pages={2680-2689},
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.},
keywords={},
doi={10.1587/transinf.E93.D.2680},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Gaussian Process Regression with Measurement Error
T2 - IEICE TRANSACTIONS on Information
SP - 2680
EP - 2689
AU - Yukito IBA
AU - Shotaro AKAHO
PY - 2010
DO - 10.1587/transinf.E93.D.2680
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - 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.
ER -
English
