Comparing and generating Latin Hypercube designs in Kriging models |
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Authors: | Giovanni Pistone Grazia Vicario |
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Institution: | (1) Department of Management Science and Technology, Nanjing University of Science and Technology, Nanjing, China;(2) Temasek Laboratories, Nanyang Technological University, 50 Nanyang Ave., Singapore, 639798, Singapore |
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Abstract: | In Computer Experiments (CE), a careful selection of the design points is essential for predicting the system response at
untried points, based on the values observed at tried points. In physical experiments, the protocol is based on Design of
Experiments, a methodology whose basic principles are questioned in CE. When the responses of a CE are modeled as jointly
Gaussian random variables with their covariance depending on the distance between points, the use of the so called space-filling
designs (random designs, stratified designs and Latin Hypercube designs) is a common choice, because it is expected that the
nearer the untried point is to the design points, the better is the prediction. In this paper we focus on the class of Latin
Hypercube (LH) designs. The behavior of various LH designs is examined according to the Gaussian assumption with exponential
correlation, in order to minimize the total prediction error at the points of a regular lattice. In such a special case, the
problem is reduced to an algebraic statistical model, which is solved using both symbolic algebraic software and statistical
software. We provide closed-form computation of the variance of the Gaussian linear predictor as a function of the design,
in order to make a comparison between LH designs. In principle, the method applies to any number of factors and any number
of levels, and also to classes of designs other than LHs. In our current implementation, the applicability is limited by the
high computational complexity of the algorithms involved. |
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Keywords: | |
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