Results 151 to 160 of about 13,495 (192)

Interpolation methods for spatial distribution of groundwater mapping electrical conductivity. [PDF]

Sci Rep
Salehi S, Barati R, Baghani M, Sakhdari S, Maghrebi M.   +4 more
europepmc   +1 more source

Geographic equity in essential newborn care practices in Ethiopia: a cross-sectional study. [PDF]

BMC Pediatr
Delele TG, Persson LÅ, Schellenberg J, Berhanu D, Lemma S, Defar A, Zemedu TG, Taye G, Shiferaw S, Denu ZA, Tariku A, Zelalem M, Gelaye KA.   +12 more
europepmc   +1 more source

Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based Implants. [PDF]

Adv Sci (Weinh)
AlBaraghtheh T, Willumeit-Römer R, Zeller-Plumhoff B.   +2 more
europepmc   +1 more source

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Universal kriging

2019
This chapter discusses universal kriging, the kriging of a random function Z(x) which is not intrinsic and exhibits an expectation E [Z(x)] = m(x) variable over the space. For the function m(x), called the drift of Z(x), a model has to be chosen, usually polynomial of degree 1, 2, or 3.
Guojun Gan, Emiliano A. Valdez
exaly   +3 more sources

Universal kriging with training images

Spatial Statistics, 2015
Abstract In the past decade, the training image (TI) has received considerable attention as a source for modeling spatial continuity in geostatistics. In this paper, the use of TIs in the context of kriging is investigated, specifically universal kriging (UK).
Lewis Li, Jef Caers
exaly   +3 more sources

Multi-event universal kriging (MEUK)

Advances in Water Resources, 2016
Multi-event universal kriging (MEUK) is a method of interpolation that creates a series of maps, each corresponding to a specific sampling “event”, which exhibit spatial relationships that persist over time. MEUK is computed using minimum-variance unbiased linear prediction from data obtained via a sequence of events.
Matthew J. Tonkin, Jonathan Kennel, William Huber, John M. Lambie   +3 more
exaly   +2 more sources

Assessment of uncertainty in computer experiments from Universal to Bayesian Kriging

Applied Stochastic Models in Business and Industry, 2009
AbstractKriging was first introduced in the field of geostatistics. Nowadays, it is widely used to model computer experiments. Since the results of deterministic computer experiments have no experimental variability, Kriging is appropriate in that it interpolates observations at data points.
Helbert, C., Dupuy, D., Carraro, L.
exaly   +2 more sources

UVKRIG: A FORTRAN-77 program for universal kriging

Computers and Geosciences, 1990
Abstract The application of universal kriging to weakly stationary data is difficult for three reasons: (1) it is difficult to estimate the variogram for the weakly stationary data; (2) the order of drift for weakly stationary random function must be modeled; and (3) equation solution differs for universal kriging from that for simple or ordinary ...
exaly   +2 more sources

Problems with universal kriging

Journal of the International Association for Mathematical Geology, 1984
exaly   +2 more sources

Universal Kriging and Cokriging as a Regression Procedure

Biometrics, 1991
Prediction of a property on the basis of a set of point measurements in a region is required if a map of this property for the region is to be made. Of the spatial interpolation and prediction techniques, kriging is optimal among all linear procedures, as it is unbiased and has minimal variance of the prediction error. In cokriging, which has this same
Stein, A., Corsten, L.C.A.
openaire   +3 more sources