Geostatistics should not be considered merely as a set of mathematical techniques for
semivariogram calculation, spatial estimation and simulation, on the basis of a (usually …

Abstract Bayesian Maximum Entropy (BME) has been successfully used in geostatistics to
calculate predictions of spatial variables given some general knowledge base and sets of …

Abstract The Bayesian Maximum Entropy (BME) method of spatial analysis and mapping
provides definite rules for incorporating prior information, hard and soft data into the …

Being a non-linear method based on a rigorous formalism and an efficient processing of
various information sources, the Bayesian maximum entropy (BME) approach has proven to …

The Bayesian maximum entropy (BME) method can be used to predict the value of a spatial
random field at an unsampled location given precise (hard) and imprecise (soft) data. It has …

This introductory scholarly treatment explores the fundamentals of modern geostatistics, a
group of spatiotemporal concepts and methods related to the advancement of the epistemic …

Due to the fast pace increasing availability and diversity of information sources in
environmental sciences, there is a real need of sound statistical mapping techniques for …

This paper is concerned with a computational formulation of the Bayesian maximum entropy
(BME) mapping method, which can handle rigorously and efficiently spatiotemporal …

The occurrence of chemical pollutants in ground water is an issue of considerable interest.
In the case of Ptolemais lignite opencast mining area in Greece, ammonium, nitrites, nitrates …

The purpose of this paper is to stress the importance of a Bayesian/maximum-entropy view
toward the spatial estimation problem. According to this view, the estimation equations …