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- W1901745588 abstract "The high-dimensionality typically associated with discretized approximations to Gaussian random fields is a considerable hinderance to computationally efficient methods for their simulation. Many direct approaches require spectral decompositions of the associated covariance matrix and so are unable to complete the solving process in a timely fashion, if at all. However under certain conditions, we may construct block-circulant versions of the covariance matrix at hand thereby allowing access to fast-Fourier methods to perform the required operations with impressive speed. We demonstrate how circulant embedding and subsequent simulation can be performed directly in the R language. The approach is currently implemented in C for the R package <b>RandomFields</b>, and used in the recently released package <b>lgcp</b>. Motivated by applications dealing with spatial point processes we restrict attention to stationary Gaussian fields on R<sup>2</sup>, where sparsity of the covariance matrix cannot necessarily be assumed." @default.
- W1901745588 created "2016-06-24" @default.
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- W1901745588 date "2013-01-01" @default.
- W1901745588 modified "2023-09-25" @default.
- W1901745588 title "On Circulant Embedding for Gaussian Random Fields in<i>R</i>" @default.
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- W1901745588 doi "https://doi.org/10.18637/jss.v055.i09" @default.
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