The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation/prediction located in a plain site of Northeastern Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, simulation methods based on most known prediction methods (as indicator Kriging and CoKriging) were implemented in spMC package. Moreover, other more advanced methods are available for simulations, e.g. path methods and Bayesian procedures, that exploit the maximum entropy. Since the spMC package was developed for intensive geostatistical computations, part of the code i...
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming fe...
The paper presents some contemporary approaches to spatial environmental data analysis. The main top...
International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for mode...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
In hydrogeology and environmental science, it can be important to assess the geological heterogeneit...
The purpose of this paper is to extend the locally based prediction methodology of BayMar to a globa...
The reconstruction of hydro-stratigraphic units in subsoil (a general term indicating all the materi...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for...
AbstractPredictions in subsurface formations consists of two steps: characterization and prediction ...
We describe the R package geoCount for the analysis of geostatistical count data. The package perfor...
Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach ...
The three-dimensional alluvial aquifer reconstruction through deterministic method from well stratig...
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming fe...
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming fe...
The paper presents some contemporary approaches to spatial environmental data analysis. The main top...
International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for mode...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation...
In hydrogeology and environmental science, it can be important to assess the geological heterogeneit...
The purpose of this paper is to extend the locally based prediction methodology of BayMar to a globa...
The reconstruction of hydro-stratigraphic units in subsoil (a general term indicating all the materi...
Scientists and investigators in such diverse fields as geological and environmen-tal sciences, ecolo...
This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for...
AbstractPredictions in subsurface formations consists of two steps: characterization and prediction ...
We describe the R package geoCount for the analysis of geostatistical count data. The package perfor...
Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach ...
The three-dimensional alluvial aquifer reconstruction through deterministic method from well stratig...
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming fe...
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming fe...
The paper presents some contemporary approaches to spatial environmental data analysis. The main top...
International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for mode...