Estimation of Gaussian process regression model using probability distance measures

Xia Hong, Junbin Gao, Xinwei Jiang, Chris J. Harris

Research output: Contribution to journalArticle

1 Citation (Scopus)
13 Downloads (Pure)


A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback'Leibler (K'L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loopto estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.
Original languageEnglish
Pages (from-to)655-663
Number of pages9
JournalSystems Science and Control Engineering
Issue number1
Publication statusPublished - Oct 2014

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