A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author

By D. Krige. 1951

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “At the same time, a large, related body of work emerged under the name ‘‘kriging,’’ in honor of the South African student who developed this technique at the University of the Witwatersrand [90], though largely popularized by Matheron and colleagues (e.”
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010, methodology): “At the same time, a large, related body of work emerged under the name kriging (§2.6), in honour of the South African student who developed this technique at the University of the Witwatersrand [Krige, 1951], though largely popularized by Matheron and colleagues (e.g. [Matheron, 1971]).”
Entropy Search for Information-Efficient Global Optimization (2011, background): “Some readers from other ﬁelds may ﬁnd it helpful to know that more or less special cases of Gaussian process inference are elsewhere known under names like Kriging (Krige, 1951) and Kolmogorov-Wiener prediction (Wiener and Masani, 1957), but while these frameworks are essentially the same idea, the ”
Multi-fidelity optimization via surrogate modelling (2007, methodology): “Krige?a South African mining engineer who pioneered the method in the 1950s for determining ore-grade distributions based on core samples (Krige 1951).”
Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “Then, kriging [29] became popular in aerodynamic shape design due to its strong fitting ability [346], and the capability of providing the predictive confidence interval, which enabled the development of the efficient global optimization method (EGO) [153].”
Active Policy Learning for Robot Planning and Exploration under Uncertainty (2008, methodology): “In this work, the regression function is obtained using Gaussian processes (GPs), originally known as kriging [36, 14, 37].”
Rigorous design of distillation columns using surrogate models based on Kriging interpolation (2015, background): “Krige in his Master Thesis.(42) The kriging fitting is composed of two parts: a polynomial expression and a deviation from that polynomial:”
Model‐based geostatistics (2007, background): “Krige, who pioneered the use of statistical methods in the South African mining industry (Krige, 1951).”
Data Assimilation for Numerical Weather Prediction: A Review (2009, background): “In atmospheric sciences use of statistical interpolation goes back to Eliassen (1954) while Krige (1951), used it in the mining industry.”
Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology (2014, methodology): “Krige, who defined a similar method for interpolation in his master’s thesis in 1951[Krige, 1951]. However, inspired by Krige’s work, Kriging was in fact developed, and the term coined, by Matheron [1962]. Perhaps the first motivation for the use of GPs in the context of Bayesian machine learning wa”
Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets (2008, background): “This computation is also expensive for large n .”
DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012, result): “For instance, Li and Sudjianto (2005) have shown some promising results obtained by Penalized MLE with SCAD penalty (Fan 1997).”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, methodology): “The statistical approach proposed by Krige [297] has become highly popular in such multidimensional interpolation problems (known as Kriging or Gaussian Process Regression [298]).”