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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Specification of Basis Spacing for Process Convolution Gaussian Process Models
verfasst von : Waley W. J. Liang, Herbert K. H. Lee
Erschienen in: Data Analysis and Rationality in a Complex World
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Abstract
Gaussian process (GP) models have been widely used for statistical modeling of point-referenced data in many scientific applications, including regression, classification, and clustering problems. Standard specification of GP models is computationally inefficient for applications with a large sample size. One solution is to construct the GP by convolving a smoothing kernel with a discretized White noise process, which requires choosing the number of bases. The distance between adjacent bases plays a key role in model accuracy. In this paper, we perform a series of simulations to find a general rule for the basis spacing required for an accurate representation of a discrete process convolution GP model. Under certain common conditions, we find that using a basis spacing of one-quarter the practical range of the process works well in practice.