The Krigifier a procedure for generating pseudorandom nonlinear objective functions for computational experimentation by Michael W. Trosset

Cover of: The Krigifier | Michael W. Trosset

Published by Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, National Technical Information Service, distributor in Hampton, VA, Springfield, VA .

Written in English

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  • Stochastic processes.,
  • Nonlinearity.,
  • Optimization.

Edition Notes

Book details

StatementMichael W. Trosset.
SeriesICASE interim report -- no. 35., [NASA contractor report] -- NASA/CR-1999-209000., NASA contractor report -- NASA CR-209000.
ContributionsInstitute for Computer Applications in Science and Engineering.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL15548922M

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Krigifier and Kriging Approximation Classes Anthony Padula, Michael W. Trosset, and Virginia Torczon While developing numerical algorithms intended to work on complex computer simulations, it is necessary to run many tests on these algorithms in order to ensure their.

Get this from a library. The Krigifier: a The Krigifier book for generating pseudorandom nonlinear objective functions for computational experimentation.

[Michael W Trosset; Institute for Computer Applications in Science and Engineering.]. The Krigifier: a random function generator The krigifier can generate a random function from one of several families of functions, which can then be evaluated at one or more points in the space. The idea for the krigifier is based on the underlying fiction of the stochastic process used in kriging.

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Author: John. In our tests, we created random test functions using the Krigifier approach [35] for several numbers of input dimensions d.

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Request PDF | Simulation-based test functions for optimization algorithms | When designing or developing optimization algorithms, test functions are crucial to evaluate performance.

Often, test. Kriging models are originated in the areas of mining and geostatistics that involve spatially and temporally correlated data. Their unique characteristic stems from their ability to combine global and local modeling.

Having a set of input-output training data [x i,y i], i = 1,2.n,x ∈ R k, the kriging assumes a general predictor y(x) = P(x) + Q(x), which is composed of a polynomial function. The Army dagger - was initially issued on May 4th, and was designed by Paul Casburg. There was only one design but it could be customized by changing the color of the handles, the type of hangers and by engraving the blade.

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Krige, who first real- ized that the problem could be tackled by the method of least squares. Notes: The contents of matrix C depends on the semi-variogram model selected by the user, its parameters nugget, sill and range, and the geometric distribution. Ordinary kriging is the most widely used kriging method.

It serves to estimate a value at a point of a region for which a variogram is known, using data in the neighborhood of the estimation.

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Estimation theory -- Computer programs. Geology -- Statistical methods -- Computer programs. View all subjects; More like this: User lists. Kriging is widely used in geostatistics to predict the response over a spatial region given data at various locations throughout the region.

A variogram firs. practice. However, since neither m(u) nor R(u) are available in reality, the spatial variability and model of m(u) is often subjective and difficult to capture mean model m(u) almost always robs the residuals of spatial variability needed for accurate spatial interpolation with the.

What is Ordinary Kriging. Definition of Ordinary Kriging: A geostatistical approach to modeling. Instead of weighting nearby data points by some power of their inverted distance, Ordinary kriging relies on the spatial correlation structure of the data to determine the weighting values.

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Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. Thanks for the response. If I am interested in comparing whether or not to use a trend removal (based on the cross-validation results), is it best to compare the results of an Ordinary/Simple Kriging interpolation (no trend removal) with the results of a.

Mathematical Geology, Vol. 22, No. 3, The Origins of Kriging 1 Noel Cressie 2 In this article, kriging is equated with spatial optimal linear prediction, where the unknown ran-dom-process mean is estimated with the best linear unbiased estimator.

This allows early appear-ances of (spatial) prediction techniques to be assessed in terms of how close they came to kriging. Universal Kriging Functionality. Universal Kriging can be seen as a point interpolation, which requires a point map as input and which returns a raster map with.

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-from Authors. My OSU colleague over in the Statistics Dept., Noel Cressie, has a book, Statistics for Spatial Data, that provides a modern update of kriging. He also has an interesting article on global kriging with Gardar Johannesson, “Fixed rank kriging for very large spatial data sets,” J.

Royal Statistical Soc B (), vol. 70,

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