The origins of kriging

By Noel Cressie. 1990

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Citations in the corpus

Grammar Variational Autoencoder (2017, methodology): “On each iteration, we select a batch of 50 latent vectors by sequen-Grammar (Cressie, 1990).”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, methodology): “To account for pending evaluations in the batch selection process we use the Kriging Believer Algorithm [38].”
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize (2018, methodology): “Interestingly, the explicit formula of Kernel Ridge Regression has been introduced as “kriging” in the literature before, and was widely used in Bayesian statistics (Cressie, 1990; Wahba, 1990).”
Recent Advances in Bayesian Optimization (2022, methodology): “Hence, the original formulation of Kriging is diferent from the GP [30].”
Living in a landscape of fear: the impact of predation, resource availability and habitat structure on primate range use (2014, unknown): “Quadrat data for each of these variables were interpolated using kriging (Cressie, 1990) to generate the landscapes (Fig.”
An efficient algorithm for Kriging approximation and optimization with large-scale sampling data (2004, methodology): “The Kriging method [21,22] is a method of spatial prediction that is based on minimizing the mean error of the weighting sum of the sampling values.”
Fixed rank kriging for very large spatial data sets (2008, background): “Krige, a South African mining engineer (Cressie, 1990).”
Continuous trajectory planning of mobile sensors for informative forecasting (2010, methodology): “Kriging [7], Gaussian processes regression (GPR) [36]) that are used to describe continuous environmental variables at an arbitrary location in terms of a finite number of variables at some specified grid points.”
A Practical Guide to Geostatistical Mapping (2009, methodology): “Matheron derived the formulas and 9 basically established the whole field of linear geostatistics17 (Cressie, 1990; Webster and Oliver, 2001).”
About predictions in spatial autoregressive models: optimal and almost optimal strategies (2017, background): “X1 follows a gaussian distributionN (15, 3),X2 follows (up to a constant) a binomial distribution B(100, 0.”
On the estimation of scale of fluctuation in geostatistics (2014, methodology): “The conditioning approach follows the work of van den Eijnden and Hicks (2011), which applied the Kriging interpolation technique (Krige, 1951; Cressie, 1990; Wackernagel, 2003; Fenton and Griffiths, 2008) to give the best linear unbiased estimate of a random field between known data.”