A Bayesian Approach to Kriging Metamodeling for Computer Experiments

In this paper, an efficient and effective Gaussian Kriging metamodeling approach is proposed in the framework of Bayesian maximum a posterior. Different prior densities and particularly, a Jeffreys' noninformative density based hierarchical prior is imposed respectively on the regression coeffi...

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Bibliographic Details
Published in:Journal of Advanced Mechanical Design, Systems, and Manufacturing Systems, and Manufacturing, 2012, Vol.6(4), pp.404-419
Main Authors: DENG, Haisong, SHAO, Wenze, MA, Yizhong
Format: Article
Language:English
Subjects:
Bayesian analysis
Computational efficiency
Computer Experiments
Density
Experimental Designs
Gaussian
Gaussian Kriging
Hierarchical Bayesian Prior
Kriging
Mathematical models
Maximization
Metamodeling
Scoring
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Summary:In this paper, an efficient and effective Gaussian Kriging metamodeling approach is proposed in the framework of Bayesian maximum a posterior. Different prior densities and particularly, a Jeffreys' noninformative density based hierarchical prior is imposed respectively on the regression coefficients in the mean model and the correlation parameters in the covariance matrix of Gaussian Kriging. And the involved parameters are estimated by the expectation maximization and Fisher's scoring algorithms. Compared against several benchmark and recent methods in literature, the proposed approach is shown capable of simultaneous high prediction accuracy and low computational cost.
ISSN:1881-3054
1881-3054
DOI:10.1299/jamdsm.6.404