Elements of information theory
Elements of information theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical tests for validating geostatistical simulation algorithms
Computers & Geosciences
A turning bands program for conditional co-simulation of cross-correlated Gaussian random fields
Computers & Geosciences
A computer package for modeling and simulating regionalized count variables
Computers & Geosciences
An enhanced Gibbs sampler algorithm for non-conditional simulation of Gaussian random vectors
Computers & Geosciences
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This article presents models of random fields with continuous univariate distributions that are defined by simple operations on stationary or intrinsic Gaussian fields. Realizations of these models can be conditioned to a set of data by using iterative algorithms based on the Gibbs sampler, while parameter inference relies on the fitting of the sample univariate and bivariate distributions. The proposed models are suited to the description of regionalized variables with a spatial clustering of high or low values, patterns of connectivity and curvilinearity, or an asymmetry in the spatial correlation of indicator variables with respect to the median threshold. The simulation procedure is illustrated by a case study in environmental science dealing with nickel concentrations in the topsoil of a polluted site.