Gaussian Process Regression with Measurement Error

スポンサーリンク

概要

• 論文の詳細を見る

• 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.

• 2010-10-01

著者

• Iba Yukito

  Department Of Statistical Modeling The Institute Of Statistical Mathematics And Department Of Statis

• AKAHO Shotaro

  Human Technology Research Institute, National Institute of Advanced Industrial Science and Technolog

• Akaho Shotaro

  Human Technology Research Institute National Institute Of Advanced Industrial Science And Technology

関連論文

• Gaussian Process Regression with Measurement Error

スポンサーリンク